Battery energy balanced distribution and optimization method, device, equipment and storage medium

Abstract

The invention relates to the technical field of battery management, and discloses a battery energy balanced distribution and optimization method, device and equipment and a storage medium. The method comprises the following steps: performing multi-source parameter acquisition and feature decoupling on each single battery in a target battery pack to obtain a single battery state data set, and performing multi-band excitation scanning and impedance spectrum fitting to obtain internal features of the battery and decompose an attenuation trend to generate equivalent circuit model parameters; performing state mapping and capacity calculation based on the model parameters to obtain a performance evaluation result of the battery pack, and constructing an energy transfer network and identifying unbalanced propagation characteristics through performance difference analysis and consistency calculation; and temperature coupling calculation and compensation matrix reconstruction are carried out based on propagation characteristics, multi-path parallel balance control is realized, and an optimization result of the battery pack is obtained through voltage deviation calculation and polarization compensation. According to the method, the multi-dimensional characteristic parameters and the dynamic change characteristics are combined, and accurate control over battery pack equalization is achieved.

Description

Technical Field

[0001] The present invention relates to the field of battery management technology, and in particular to a battery energy balanced distribution and optimization method, device, equipment and storage medium. Background Art

[0002] In the field of battery pack management technology, energy balance distribution and optimization are key links to ensure safe and stable operation of battery packs, and accurate evaluation and balanced control of battery pack single cell status is the most challenging stage in energy balancing. Accurately evaluating the operating status of each single cell in a battery pack is crucial for manufacturers, R&D institutions, and application units, as it directly affects the service life, charge and discharge efficiency, and overall performance of the battery pack. Therefore, an effective energy balancing method to optimize the energy distribution of the battery pack is essential to ensure the safety and consistency of the battery pack.

[0003] With the development of artificial intelligence technology, researchers have begun to try to apply machine learning methods to battery state estimation and balancing control. However, these methods still face challenges in processing multi-dimensional parameter characteristics, modeling polarization effects, and adapting to dynamic working conditions, and often ignore some unique characteristics of batteries, such as temperature effects, internal resistance drift, self-discharge and other factors that affect the balancing effect. That is, the existing battery energy balancing method ignores the multi-dimensional characteristic parameters and dynamic change characteristics of the battery pack, resulting in unsatisfactory final balancing control effect, affecting the service life of the battery pack. Summary of the invention

[0004] The main purpose of the present invention is to solve the problem that the existing battery energy balancing method ignores the multi-dimensional characteristic parameters and dynamic change characteristics of the battery pack, resulting in an unsatisfactory final balancing control effect and affecting the service life of the battery pack.

[0005] The first aspect of the present invention provides a battery energy balanced distribution and optimization method, the battery energy balanced distribution and optimization method comprising: collecting multi-source original parameter data of each single cell in a target battery pack, and extracting multi-source features and separating single cell states of the multi-source original parameter data to obtain a single cell state data set; performing multi-band AC excitation scanning and impedance spectrum fitting on the single cell state data set to obtain battery internal characteristic parameters, and performing cycle aging feature identification and capacity attenuation trend decomposition on the battery internal characteristic parameters to obtain battery equivalent circuit model parameters; based on the battery equivalent circuit model parameters, performing model mapping and state decoupling on the battery state data set to obtain battery state quantitative characteristics, and performing charge and discharge capacity integral calculation and cycle loss calculation on the battery state quantitative characteristics to obtain the performance of the target battery pack. energy evaluation results; performing performance difference calculation and voltage consistency calculation of single-cell batteries on the performance evaluation results to obtain single-cell capacity distribution data, and performing energy transfer network construction and propagation path identification between single-cell batteries on the battery pack imbalance parameters to obtain energy imbalance propagation characteristics; performing boundary calculation of multi-battery variable charge and temperature coupling and reconstruction of the state of charge difference compensation matrix on the energy imbalance propagation characteristics to generate battery pack balancing parameters, and based on the battery pack balancing parameters, performing multi-channel parallel control on the target battery pack to obtain the state of charge curve and polarization voltage data of each single cell in the battery pack; performing voltage deviation calculation on the state of charge curve and the polarization voltage data to generate a voltage difference matrix of each single-cell battery, and performing polarization compensation for voltage difference on the voltage difference matrix to obtain the battery optimization result of the target battery pack.

[0006] Optionally, in a first implementation method of the first aspect of the present invention, the multi-source original parameter data is extracted with multi-source features and separated with single cell states to obtain a single cell battery state data set, including: extracting high-frequency components of the multi-source original parameter data to obtain high-frequency interference data, and performing frequency threshold truncation on the high-frequency interference data to obtain filtered multi-source original parameter data; segmenting the filtered multi-source original parameter data into voltage curves to obtain charge and discharge conversion point data, and identifying the working state mode of the charge and discharge conversion point data to obtain working state marking data; extracting the constant current interval of the working state marking data to obtain working polarization interval data, and performing step response calculation on the working polarization interval data to obtain a battery pack working characteristic curve; performing capacity matching on the battery pack working characteristic curve to obtain single cell characteristic data, and calibrating the deviation coefficient of the single cell characteristic data to obtain a single cell battery state data set.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the single cell battery status data set is subjected to multi-band AC excitation scanning and impedance spectrum fitting to obtain internal characteristic parameters of the battery, and the internal characteristic parameters of the battery are subjected to cycle aging feature identification and capacity attenuation trend decomposition to obtain battery equivalent circuit model parameters, including: extracting the operating cycle characteristics of the corresponding constant current charge and discharge interval of the single cell battery status data set to obtain cycle characteristic data; extracting the constant voltage interval and the dynamic response characteristics of the cycle characteristic data to obtain charge and discharge time domain characteristic data, and performing pulse response decomposition and harmonic characteristic analysis on the charge and discharge time domain characteristic data. to obtain charge and discharge frequency domain characteristic data; to perform AC impedance calculation and charge transfer impedance separation on the frequency domain characteristic data to obtain impedance characteristic data, and to perform capacity loss calculation on the impedance characteristic data to obtain a battery performance decay curve; to perform life characteristic extraction on the performance decay curve to obtain battery decay characteristic data, and to perform identification of preset multiple polarization parameters on the battery decay characteristic data to obtain battery polarization characteristic parameters; to perform conversion calculation of battery internal resistance and equivalent capacitance on the battery polarization characteristic parameters to obtain initial circuit equivalent parameters, and to perform circuit closed-loop calibration on the initial circuit equivalent parameters to obtain battery equivalent circuit model parameters.

[0008] Optionally, in a third implementation method of the first aspect of the present invention, based on the battery equivalent circuit model parameters, the battery state data set is model mapped and state decoupled to obtain battery state quantitative characteristics, and the charge and discharge capacity integral calculation and cycle loss calculation are performed on the battery state quantitative characteristics to obtain the performance evaluation result of the target battery pack, including: separating the polarization parameters of the battery equivalent circuit model parameters to obtain internal resistance characteristic data, and based on the temperature response coefficient corresponding to the target battery pack, performing temperature coefficient compensation on the internal resistance characteristic data to obtain temperature compensation data; mapping the working state characteristics of the temperature compensation data to obtain basic electrical parameters, and calculating the corresponding internal resistance change values ​​and polarization parameter change values ​​of the basic electrical parameters. , obtain electrical characteristic change data; identify the charge and discharge cycle characteristics of the electrical characteristic change data to obtain cycle characteristic data, and divide the discharge depth of the single cell and calculate the charge and discharge capacity ratio of the cycle characteristic data to obtain the state of charge data; calculate the capacity decay rate and the remaining life of the state of charge data to obtain the single cell health status data, and calculate the maximum discharge capacity of the single cell health status data to obtain the single cell actual capacity data; based on the preset single cell capacity corresponding to the target battery pack, compare the capacity difference of the single cell actual capacity data to obtain the battery pack capacity distribution characteristic data, and calculate the capacity mean dispersion of the battery pack capacity distribution characteristic data to obtain the performance evaluation result of the target battery pack.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the performance evaluation results are subjected to performance difference calculation and voltage consistency calculation of single-cell batteries to obtain single-cell capacity distribution data, and the battery pack imbalance parameter is subjected to energy transfer network construction and propagation path identification between single-cell batteries to obtain energy imbalance propagation characteristics, including: performing single-cell capacity sorting and capacity difference calculation of adjacent single-cell batteries on the performance evaluation results of the battery pack to obtain performance difference data, and grading the performance difference data according to a preset capacity difference threshold to obtain capacity grade data; performing three-dimensional single-cell battery position mapping and capacity distribution statistics on the capacity grade data to obtain single-cell battery capacity distribution data. Capacity distribution characteristic data, and calculate the voltage difference between monomers and pair the nearest neighboring monomer batteries for the monomer capacity distribution characteristic data to obtain the network structure data between the monomer batteries; identify the current flow direction and search the shortest path of energy flow for the network structure data to obtain energy propagation path data, and identify the series-parallel connection relationship and calculate the energy transmission impedance for the propagation path data to obtain path weight data; calculate the energy distribution and energy density of the monomer batteries for the path weight data to obtain the battery energy space distribution data, and identify the balanced loop and calculate the transmission efficiency for the battery energy space distribution data to generate energy imbalance propagation characteristics.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the energy imbalance propagation characteristics are subjected to boundary calculation of multi-battery variable power and temperature coupling and reconstruction of the charge state difference compensation matrix to generate battery pack balancing parameters, and based on the battery pack balancing parameters, the target battery pack is subjected to multi-channel parallel control to obtain the charge state curve and polarization voltage data of each cell in the battery pack, including: performing transmission path sorting and energy level allocation on the energy imbalance propagation characteristics to obtain energy balancing priority data, and performing distance calculation between single cell batteries and normalization calculation of energy transmission path distance on the balancing priority data to obtain optimized single cell target data; performing single cell voltage difference calculation on the single cell optimization target data to obtain voltage working The method comprises the following steps: performing interval data, and performing battery operating temperature boundary calculation on the voltage operating interval data to obtain temperature and voltage interval data; performing balancing time interval division and closed-loop path calculation on the temperature and voltage interval data to obtain multi-channel transmission path data, and performing energy transmission capacity calculation and minimum transmission loss sorting on the multi-channel transmission path data to generate battery pack balancing parameters; performing multi-channel balancing current detection on each single cell of the battery pack balancing parameters to obtain single cell control current data, and performing single cell charge accumulation calculation on the single cell control current data to obtain single cell capacity change data; performing single cell charge state calculation and single cell polarization voltage calculation on the single cell capacity change data to obtain charge state curve and polarization voltage data of each single cell of the battery pack.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the voltage deviation calculation is performed on the state of charge curve and the polarization voltage data to generate a voltage difference matrix of each single cell, and the voltage difference polarization compensation is performed on the voltage difference matrix to obtain the battery optimization result of the target battery pack, including: dividing the state of charge curve of each single cell into time periods and calculating the charge change per unit time to obtain state of charge gradient data, and accumulating the sampling period and extracting the fluctuation amplitude of the polarization voltage data of each single cell to obtain voltage fluctuation characteristic data, and performing polarization response rate calculation and single cell polarization strength calculation on the state of charge gradient data to obtain a polarization characteristic matrix; extracting the single cell polarization strength and response time of the polarization characteristic matrix to obtain a single cell polarization factor, and performing correlation difference calculation on the single cell polarization factor and the voltage fluctuation characteristic data to generate a voltage difference matrix of each single cell; performing single cell voltage compensation calculation on the voltage difference matrix to obtain single cell voltage correction data, and performing average voltage alignment on the single cell voltage correction data to obtain the battery optimization result of the target battery pack.

[0012] The second aspect of the present invention provides a battery energy balanced distribution and optimization device, which includes: a state separation module, which is used to collect multi-source original parameter data of each single battery in the target battery pack, and extract multi-source features and separate single states of the multi-source original parameter data to obtain a single battery state data set; a trend decomposition module, which is used to perform multi-band AC excitation scanning and impedance spectrum fitting on the single battery state data set to obtain battery internal characteristic parameters, and perform cycle aging feature identification and capacity attenuation trend decomposition on the battery internal characteristic parameters to obtain battery equivalent circuit model parameters; a performance evaluation module, which is used to perform model mapping and state decoupling on the battery state data set based on the battery equivalent circuit model parameters to obtain battery state quantitative characteristics, and perform charge and discharge capacity integral calculation and cycle loss calculation on the battery state quantitative characteristics to obtain the performance of the target battery pack. energy evaluation results; a path identification module, used to perform performance difference calculation and voltage consistency calculation of single-cell batteries on the performance evaluation results, obtain single-cell capacity distribution data, and construct an energy transfer network and identify the propagation path between single-cell batteries for the battery pack imbalance parameter, and obtain energy imbalance propagation characteristics; a parallel control module, used to perform boundary calculation of multi-battery variable charge and temperature coupling and reconstruction of the state of charge difference compensation matrix for the energy imbalance propagation characteristics, generate battery pack balancing parameters, and based on the battery pack balancing parameters, perform multi-channel parallel control on the target battery pack to obtain the state of charge curve and polarization voltage data of each single cell in the battery pack; a battery optimization module, used to perform voltage deviation calculation on the state of charge curve and the polarization voltage data, generate a voltage difference matrix of each single cell, and perform polarization compensation for voltage difference on the voltage difference matrix to obtain the battery optimization result of the target battery pack.

[0013] The third aspect of the present invention provides a battery energy balanced distribution and optimization device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the battery energy balanced distribution and optimization device performs each step of the above-mentioned battery energy balanced distribution and optimization method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute each step of the above-mentioned battery energy balanced distribution and optimization method.

[0015] The above-mentioned battery energy balanced distribution and optimization method, device, equipment and storage medium. In the embodiment of the present invention, by collecting multi-source original parameter data of each single cell in the target battery pack, and extracting multi-source features and separating single cell states from the multi-source original parameter data, a single cell state data set is obtained; multi-band AC excitation scanning and impedance spectrum fitting are performed on the single cell state data set to obtain the internal characteristic parameters of the battery, and cycle aging characteristics and capacity attenuation trend decomposition are performed on the internal characteristic parameters of the battery to obtain the battery equivalent circuit model parameters; based on the battery equivalent circuit model parameters, model mapping and state decoupling are performed on the battery state data set to obtain the battery state quantitative characteristics, and charge and discharge capacity integral calculation and cycle loss calculation are performed on the battery state quantitative characteristics to obtain the performance evaluation results of the target battery pack; the performance evaluation results are evaluated. The performance difference and voltage consistency of single cells are calculated to obtain the single cell capacity distribution data, and the energy transfer network and propagation path identification between single cells are carried out for the battery pack imbalance parameters to obtain the energy imbalance propagation characteristics; the boundary calculation of multi-battery variable power and temperature coupling and the charge state difference compensation matrix are carried out for the energy imbalance propagation characteristics to generate the battery pack balancing parameters, and based on the battery pack balancing parameters, the target battery pack is controlled in multiple parallel channels to obtain the charge state curve and polarization voltage data of each single cell in the battery pack; the voltage deviation is calculated for the charge state curve and polarization voltage data to generate the voltage difference matrix of each single cell, and the voltage difference polarization compensation is performed on the voltage difference matrix to obtain the battery optimization result of the target battery pack. Compared with the prior art, this application acquires basic battery state characteristics by multi-source parameter collection and feature decoupling of each single cell in the target battery pack, and then obtains the internal characteristics of the battery and decomposes the attenuation trend through multi-band excitation scanning and impedance spectrum fitting, and generates equivalent circuit model parameters; then, based on this model parameter, state mapping and capacity calculation are performed to obtain the performance evaluation result of the battery pack; then, through performance difference analysis and consistency calculation, an energy transfer network is constructed and unbalanced propagation characteristics are identified; finally, temperature coupling calculation and compensation matrix reconstruction are performed based on propagation characteristics to achieve multi-channel parallel balancing control, and the optimization result of the battery pack is obtained through voltage deviation calculation and polarization compensation. Through hierarchical data and feature analysis, precise control of battery pack balancing is achieved, especially in terms of charge state consistency, polarization characteristics and capacity balancing, the dynamic characteristics and unbalanced characteristics of the battery pack are fully considered, and the balancing effect is effectively improved; and the multi-level feature decoupling and state mapping strategy are adopted to realize the correlation analysis between battery parameters and enhance the reliability of balancing control; in addition, through propagation feature identification and compensation optimization, the balancing process is accurately controlled, thereby achieving efficient and accurate balancing of the battery pack as a whole.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a first embodiment of a battery energy balanced distribution and optimization method according to an embodiment of the present invention; Figure 2 A schematic diagram of an embodiment of a battery energy balanced distribution and optimization device in an embodiment of the present invention; Figure 3 It is a schematic diagram of an embodiment of a battery energy balanced distribution and optimization device in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0021] To facilitate understanding of this embodiment, the specific process of the embodiment of the present invention is described below. Figure 1 The first embodiment of the battery energy balanced distribution and optimization method in the embodiment of the present invention includes: 101. Collect multi-source original parameter data of each single battery in the target battery pack, extract multi-source features and separate single battery states from the multi-source original parameter data, and obtain a single battery state data set; The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0022] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0023] In this embodiment, high-frequency components of multi-source original parameter data are extracted to obtain high-frequency interference data, and the high-frequency interference data is truncated by frequency threshold to obtain filtered multi-source original parameter data; voltage curve segmentation is performed on the filtered multi-source original parameter data to obtain charge and discharge conversion point data, and the working state mode is identified on the charge and discharge conversion point data to obtain working state marking data; constant current interval is extracted from the working state marking data to obtain working polarization interval data, and step response calculation is performed on the working polarization interval data to obtain the battery pack working characteristic curve; capacity matching is performed on the battery pack working characteristic curve to obtain single cell characteristic data, and the deviation coefficient of the single cell characteristic data is calibrated to obtain a single cell battery state data set.

[0024] In practical applications, a distributed data acquisition network is used to collect multi-source parameters of each single cell in the target battery pack. The collected data include basic electrical parameters such as voltage, current, and temperature. At the same time, a high-precision sensor array is used to collect parameters such as the impedance characteristics and thermal characteristics of the battery (for example, the ripple characteristics of the battery terminal voltage are sampled at 500Hz to obtain voltage fluctuation data; the current pulse response during the charge and discharge process is sampled at 2kHz to obtain current characteristic curve data; the battery surface temperature field is scanned by infrared thermal imaging to obtain temperature distribution map data; the battery electrochemical impedance spectrum is frequency scanned to obtain impedance characteristic parameter data, etc.). After the acquisition is completed, the high-frequency components of these original parameter data are extracted, and their spectral characteristics are analyzed by FFT transformation to identify the high-frequency interference components introduced by factors such as measurement equipment and environmental interference, and then the frequency threshold is set (usually 1 / 4 of the sampling frequency), the high-frequency interference data is truncated, and the high-frequency noise is eliminated by a digital filtering algorithm, and finally the high-quality original parameter data after filtering is obtained; the filtered data is then segmented into voltage curves, that is, the voltage change rate and inflection point characteristics are analyzed and identified. Charge and discharge conversion points, which reflect the change process of the battery working state, and based on these conversion point data, combined with the change characteristics of parameters such as current and temperature, the pattern recognition algorithm is used to classify the battery working state, including different working modes such as charging, discharging, and shelving, so as to obtain working state data with state labels; then the marked data is extracted in constant current intervals (where these intervals can better reflect the polarization characteristics of the battery), and the working polarization interval of the battery is determined by analyzing the voltage response curve in the constant current interval, and a standard current step excitation is applied to the battery in this interval, and the voltage response process is recorded. The step response characteristics of the battery are calculated using the system identification method, so as to obtain the working characteristic curve reflecting the dynamic characteristics of the battery; then the obtained working characteristic curve is matched with the standard capacity curve, and the characteristic data of each single cell is obtained by comparing the characteristic points and fitting the curve, and considering the influence of measurement error and system error, the deviation coefficient of these characteristic data is calibrated, and the characteristic parameters are corrected based on historical data and calibration results by establishing an error compensation model, and finally a data set that accurately reflects the working state of each single cell is formed.

[0025] 102. Perform multi-band AC excitation scanning and impedance spectrum fitting on the single battery state data set to obtain the internal characteristic parameters of the battery, and perform cycle aging feature recognition and capacity attenuation trend decomposition on the internal characteristic parameters of the battery to obtain the battery equivalent circuit model parameters; In this embodiment, the operating cycle characteristics of the corresponding constant current charge and discharge interval are extracted from the single cell state data set to obtain cycle characteristic data; the constant voltage interval and dynamic response characteristics are extracted from the cycle characteristic data to obtain charge and discharge time domain characteristic data, and the charge and discharge time domain characteristic data are subjected to pulse response decomposition and harmonic feature reconstruction to obtain charge and discharge frequency domain characteristic data; the frequency domain characteristic data are subjected to AC impedance calculation and charge transfer impedance separation to obtain impedance characteristic data, and the impedance characteristic data are subjected to capacity loss calculation to obtain the battery performance decay curve; the performance decay curve is subjected to life characteristic extraction to obtain battery decay characteristic data, and the battery decay characteristic data is subjected to preset multiple polarization parameters identification to obtain battery polarization characteristic parameters; the battery polarization characteristic parameters are subjected to conversion calculation of battery internal resistance and equivalent capacitance to obtain initial circuit equivalent parameters, and the initial circuit equivalent parameters are subjected to circuit closed-loop calibration to obtain battery equivalent circuit model parameters. The above-mentioned preset multiple polarization parameters refer to concentration polarization and electrochemical polarization parameters.

[0026] In practical applications, the constant current charge and discharge interval is determined by identifying the characteristics of the voltage and current curves of the single battery state data set. The cycle characteristics of the battery operation are detected within this interval, including the charge and discharge time, voltage change trend and current stability, so as to extract data reflecting the battery cycle working characteristics. Through these cycle characteristic data, the operation characteristics of the battery in the constant voltage charging stage are identified. At the same time, the dynamic response characteristics of the battery when the load changes, including time domain characteristic parameters such as voltage drop and current response speed, are extracted to obtain complete charge and discharge time domain characteristic data. Pulse excitation is applied to these time domain data to analyze the transient response characteristics of the battery. The time domain characteristics are decomposed into different frequency components through Fourier transform, and the harmonic characteristics of the battery during the charge and discharge process are reconstructed, and finally the charge and discharge frequency domain characteristic data are obtained; then the AC impedance characteristics of the battery are calculated by fitting the complex impedance spectrum through the nonlinear least squares method, and the charge transfer impedance component is separated using the equivalent circuit model. The impedance calculation formula is: ; in, is the ohmic internal resistance, is the charge transfer resistance, τ is the time constant, is the Warburg impedance coefficient (describing the diffusion impedance strength of the physical ions in the electrolyte and the electrode), ω is the angular frequency (for example, at 25°C, the typical 18650 lithium battery R0 is about 20mΩ, and Rct is about 15mΩ). These impedance characteristic data reflect the electrochemical reaction state and ion transport characteristics inside the battery; based on the impedance characteristic data, combined with the number of charge and discharge cycles and the actual capacity measurement results, the loss calculation formula is used to generate the attenuation curve, and the formula is: ; in, is the exchange current density, α and β are the transfer coefficients, n is the number of electron transfers, F is the Faraday constant, and E-E0 is the overpotential, so as to calculate the capacity loss of the battery during use and obtain a decay curve reflecting the change of battery performance over time (wherein, this curve clearly shows the trend and law of battery capacity decay); then, the key parameters reflecting the battery life characteristics are extracted from the obtained performance decay curve, including capacity retention rate, internal resistance growth rate, etc., to form a battery decay characteristic data set, and based on the pre-set concentration polarization and electrochemical polarization parameter models, the polarization characteristics of the decay characteristic data are identified, and the polarization parameter values ​​under different working conditions are determined to obtain a complete battery polarization characteristic parameter set (wherein, these polarization characteristic parameters are converted into specific electrical parameters through a specific mathematical model, including ohmic internal resistance and double-layer capacitance equivalent values, so as to establish a preliminary circuit equivalent parameter model); then, the composite Thevenin equivalent circuit model formula is used to generate the equivalent circuit model parameters, and the formula is: ; in, is the battery terminal voltage (or load voltage), which changes with time t. is the open circuit voltage, I is the battery current, is the ohmic internal resistance, R k is the polarization resistance, τ k is the time constant, n is the RC network order (usually 2 or 3), and the recursive least squares method is used to perform parameter identification and closed-loop calibration, and finally accurate equivalent circuit model parameters are obtained, that is, by comparing the error between the model prediction value and the measured data, the model parameters are continuously adjusted until the error converges to within the preset threshold, and finally accurate battery equivalent circuit model parameters are obtained, where these parameters not only reflect the current state of the battery, but also include the dynamic characteristics and aging characteristics of the battery. Through multi-level feature extraction and parameter identification, accurate mapping from battery state data to equivalent circuit model is achieved.

[0027] 103. Based on the battery equivalent circuit model parameters, perform model mapping and state decoupling on the battery state data set to obtain the battery state quantitative characteristics, and perform charge and discharge capacity integral calculation and cycle loss calculation on the battery state quantitative characteristics to obtain the performance evaluation result of the target battery pack; In this embodiment, the polarization parameters of the battery equivalent circuit model parameters are separated to obtain internal resistance characteristic data, and based on the temperature response coefficient corresponding to the target battery pack, the internal resistance characteristic data is compensated for the temperature coefficient to obtain temperature compensation data; the working state characteristics are mapped to the temperature compensation data to obtain basic electrical parameters, and the corresponding internal resistance change values ​​and polarization parameter change values ​​of the basic electrical parameters are calculated to obtain electrical characteristic change data; the charge and discharge cycle characteristics of the electrical characteristic change data are identified to obtain cycle characteristic data, and the cycle characteristic data are divided into discharge depths of single cells and the charge and discharge capacity ratio is calculated to obtain state of charge data; the capacity decay rate and the remaining life are calculated for the state of charge data to obtain single cell health status data, and the maximum discharge capacity is calculated for the single cell health status data to obtain single cell actual capacity data; based on the preset single cell capacity corresponding to the target battery pack, the capacity difference of the single cell actual capacity data is compared to obtain battery pack capacity distribution characteristic data, and the capacity mean dispersion is calculated for the battery pack capacity distribution characteristic data to obtain the performance evaluation result of the target battery pack. The single cell capacity here refers to the nominal capacity value specified by the battery manufacturer, that is, the theoretical amount of power that the battery can provide under standard conditions.

[0028] In practical applications, concentration polarization and electrochemical polarization parameters are separated from the obtained battery equivalent circuit model parameters, that is, parameter identification is performed through nonlinear least squares method, and the identification equation is: ; in, is the polarization internal resistance, R0 is the ohmic internal resistance, is the charge transfer internal resistance, R d is the diffusion resistance, τ and τ d are the charge transfer and diffusion time constants, respectively. By solving this equation, different types of polarization internal resistance characteristics can be separated (for example, for a lithium battery, under 1C discharge conditions, R0=10mΩ, R ct =8mΩ, R d =5mΩ,τ=10s,τ d=100s), thereby calculating the characteristic parameters of the battery such as ohmic internal resistance and polarization internal resistance, and forming a complete internal resistance characteristic data set; and based on the temperature response characteristics of the target battery pack, a temperature-internal resistance correspondence model is established, and the internal resistance characteristics are compensated and corrected through real-time measured temperature data to obtain compensation data that eliminates the influence of temperature (this data reflects the intrinsic electrochemical characteristics of the battery); and then the temperature-compensated data is mapped to the actual working state of the battery, and a parameter set reflecting the basic electrical characteristics of the battery is obtained by establishing a state-parameter correspondence relationship, and based on these basic electrical parameters, the internal resistance change and polarization parameter change of the battery under different working conditions are calculated (these changes directly reflect the dynamic response characteristics and performance degradation trends of the battery), thereby obtaining complete electrical characteristic change data; and then the corresponding battery in the electrical characteristic change data during charging and discharging is identified. The periodic characteristics of the electrical process, including charge and discharge time, voltage platform and current change law, form a cycle characteristic data set, and based on the cycle characteristic data, accurately divide the discharge depth of each single cell, and calculate its charge and discharge capacity ratio (where these data directly reflect the battery's charge and discharge efficiency and charge state), so that the battery's charge state can be accurately evaluated through these data; then the battery capacity decay rate is calculated for the obtained charge state data, and the remaining service life of the battery is predicted based on the decay trend to form a battery health status data set, and the maximum discharge capacity of each single cell is obtained through a standard discharge test to reflect the actual available capacity of the battery; then the actual capacity data of the single cell is compared and analyzed with the preset standard capacity of the battery pack to calculate the capacity difference, and these difference data reflect the consistency level within the battery pack. Through the calculation of capacity decay rate λ and remaining life L: , ; Among them, C n is the capacity after the nth cycle, C0 is the initial capacity (indicating the maximum capacity of the battery when it leaves the factory or is used for the first time), C th is the capacity threshold (the actual capacity of the battery after the nth cycle), ΔT is the cycle period, and n is the number of cycles, that is, the number of charge and discharge cycles the battery has experienced (for example, a battery with an initial capacity of 3000mAh has a capacity of 2850mAh after 100 cycles, and a capacity threshold of 2400mAh, and λ=0.0005 / cycle can be calculated, and the estimated remaining life is about 300 cycles), so as to evaluate the discreteness of the capacity distribution of the battery pack and finally obtain an evaluation result that fully reflects the performance status of the battery pack. This evaluation result not only includes the overall performance level of the battery pack, but also reflects the consistency between the single cells.

[0029] 104. Calculate the performance difference and voltage consistency of the single cells based on the performance evaluation results to obtain the single cell capacity distribution data, and construct the energy transfer network and identify the propagation path between the single cells based on the battery pack imbalance parameter to obtain the energy imbalance propagation characteristics; In this embodiment, the performance evaluation results of the battery pack are sorted by single cell capacities and the capacity differences of adjacent single cells are calculated to obtain performance difference data, and the performance difference data are graded according to preset capacity difference thresholds to obtain capacity grade data; the capacity grade data are mapped by three-dimensional single cell positions and capacity distribution statistics to obtain single cell capacity distribution characteristic data, and the single cell capacity distribution characteristic data are calculated by calculating the voltage difference between single cells and pairing the nearest neighbor single cells to obtain network structure data between single cells; the network structure data are identified by current flow direction and the shortest path search for energy flow to obtain energy propagation path data, and the propagation path data are identified by identifying the series-parallel connection relationship and calculating the energy transmission impedance to obtain path weight data; the path weight data are calculated by energy distribution and energy density of single cells to obtain battery energy spatial distribution data, and the battery energy spatial distribution data are identified by balancing loops and transmission efficiency calculation to generate energy imbalance propagation characteristics. The capacity difference threshold here refers to the standard value used to measure the degree of inconsistency in the capacity of adjacent single cells. For example, it can be expressed as a percentage of the nominal capacity, such as 3%, 5% and 8%. The corresponding limit value can be set according to the battery pack's requirements for balance.

[0030] In practical applications, based on the battery pack performance evaluation results, the actual capacity values ​​of the single cells are sorted in descending order, and the capacity difference between adjacent cells is calculated. The capacity difference calculation formula is: ; Among them, ΔC(i, j) represents the comprehensive difference between the i-th and j-th monomers, C i , V i , T i Represent the capacity of the i-th monomer, C i , V i , T i Represent the capacity, voltage and temperature of the i-th monomer, C nom , V nom , T nomis the nominal value (i.e. the reference capacity of the single cell in the battery pack (usually the design capacity or theoretical capacity), the reference voltage of the single cell in the battery pack, i.e. the reference temperature of the single cell in the battery pack (usually the standard test temperature), α is the temperature influence weight coefficient (for example: for a 48V battery pack, the parameters of two adjacent single cells are: C1=2000mAh, V1=3.8V, T1=25℃, C2=1950mAh, V2=3.75V, T2=27℃, and then substitute them into the formula to calculate the difference). These difference data directly reflect the capacity imbalance within the battery pack; and according to the pre-set capacity difference threshold (usually 3%, 5% and 8% of the nominal capacity), all single cells are divided into different capacity levels to form hierarchical capacity level data. This grading method can identify single cells with significant capacity differences; and then combine the capacity level data with the physical structure of the battery pack to establish a three-dimensional spatial coordinate system, map the actual installation position of each single cell to the coordinate system, and perform statistical analysis based on the capacity distribution to generate a data set reflecting the spatial distribution characteristics of the battery pack. Based on these distribution characteristic data, the voltage difference between physically adjacent cells is calculated, and the nearest neighbor algorithm is used to pair the single cells. This pairing takes into account both the constraints of physical distance and the need for voltage difference balance. Finally, network structure data representing the correlation between single cells is formed; based on the network structure data, the potential flow direction of the current is determined by potential difference calculation, and the Dijkstra algorithm is used to search for the shortest path for energy transmission. These paths represent the optimal channels for energy to flow inside the battery pack, and based on the searched path data, the series and parallel connection relationship in the battery pack is identified, and the equivalent impedance on each transmission path is calculated in combination with the internal resistance characteristics of the single cells. These data constitute a complete path weight data set; based on the path weight data, the energy distribution status of each single cell in the battery pack is calculated, including characteristics such as energy storage capacity and energy density, forming spatial characteristic data reflecting the energy distribution of the battery pack, thereby identifying potential balancing loops and calculating the energy transmission efficiency of each loop, and finally forming energy imbalance propagation characteristics. These characteristics not only reflect the current energy distribution status of the battery pack, but also reveal the propagation law and balancing potential of energy inside the battery pack.

[0031] 105. Perform boundary calculation of multi-battery variable power and temperature coupling and reconstruction of charge state difference compensation matrix for energy imbalance propagation characteristics, generate battery pack balancing parameters, and perform multi-channel parallel control on the target battery pack based on the battery pack balancing parameters to obtain the charge state curve and polarization voltage data of each cell of the battery pack; In this embodiment, the energy imbalance propagation characteristics are sorted by transmission paths and energy levels to obtain energy balancing priority data, and the distance between single cells and the normalization calculation of the energy transmission path distance are performed on the balancing priority data to obtain optimized single cell target data; the single cell voltage difference calculation is performed on the single cell optimization target data to obtain voltage working range data, and the battery working temperature boundary calculation is performed on the voltage working range data to obtain temperature and voltage range data; the temperature and voltage range data are divided into balancing time intervals and closed-loop path calculations are performed to obtain multi-channel transmission path data, and the energy transmission capacity calculation and minimum transmission loss sorting are performed on the multi-channel transmission path data to generate battery pack balancing parameters; the battery pack balancing parameters are subjected to multi-channel balancing current detection of each single cell to obtain single cell control current data, and the single cell charge accumulation calculation is performed on the single cell control current data to obtain single cell capacity change data; the single cell charge state calculation and single cell polarization voltage calculation are performed on the single cell capacity change data to obtain the charge state curve and polarization voltage data of each single cell of the battery pack.

[0032] In practical applications, based on the energy imbalance propagation characteristics, the energy efficiency and distance of the transmission path are prioritized, and the energy transmission level is assigned in combination with the capacity level of each single battery, thereby generating energy balance priority data. Based on the physical layout corresponding to the battery pack, the actual distance between the single batteries that need to transmit energy is calculated, and the improved Minkowski distance calculation formula is used to normalize the distance data and map it to the [0, 1] interval through the maximum and minimum values. The calculation formula is: ; Among them, x i ,y i is the spatial coordinate of the single battery, T i , T j For the and The temperature of each single cell, p, q are distance indexes, λ is the temperature weight coefficient, and r is the normalized index (for example: for two adjacent cells, the coordinate difference is (0.1, 0.2, 0.1), the temperature difference is 5°C, p=2, q=2, r=2, λ=0.1, the normalized distance D=0.583 can be obtained), so that the distance factor can be comprehensively weighed with the energy transmission efficiency, and finally the optimized single cell target data is formed; then, for each optimized target cell, the voltage difference between it and the adjacent cell is calculated, and the feasible voltage working range is determined in combination with the safe working voltage range of the battery (usually 2.5V-4.2V), and considering the significant influence of temperature on battery performance, the temperature boundary is set according to the working temperature range of the battery (usually 0℃-45℃), and the voltage working range is combined with the temperature boundary to form a dual constraint interval number of temperature and voltage. Based on these constraint data, the balancing process is divided into multiple time intervals, and the closed-loop transmission path is calculated in each interval to ensure the continuity and stability of energy transmission, thereby obtaining multi-path parallel transmission path data; then the maximum transmittable capacity of each transmission path is calculated, which is usually no more than 80% of the difference in monomer capacity to avoid over-balancing. At the same time, considering the transmission loss factor, all feasible paths are sorted from small to large in terms of loss, and the balancing control parameters of the battery pack are generated by integrating these data; then based on these balancing parameters, each balancing loop is monitored in real time through a high-precision current sensor network to obtain the control current data of each monomer battery participating in the balancing process, and the dynamic state of charge estimation equation is established through these current data to calculate the charge change data of the monomer battery during the balancing process. The dynamic state of charge estimation equation is: ; in: is the state of charge of the battery at the current time t (range: [0, 1]), SOC0 is the initial state of charge, is the current of the battery at time t (unit: A, discharge is positive, charge is negative), η(I, T) is the coulomb efficiency (indicates the efficiency of charge conversion during charging and discharging, affected by current and temperature T), Qn is the nominal capacity, K(T, t) is the capacity correction coefficient, ∆Q(t) is the capacity loss term (indicating the effective capacity loss of the battery due to aging or other effects within t time) (for example: the initial SOC of a single cell is 0.8, and it is discharged at a rate of 0.5C for 30 minutes, considering 90% coulomb efficiency and 0.2% / h capacity loss, and thus calculating the final SOC state); then based on the accumulated charge change data, by combining the capacity characteristics of the battery, the real-time charge state of each single cell is calculated, and the polarization effect during the balancing process is considered to calculate the polarization voltage response of the single cell, and these data ultimately form a complete charge state change curve and polarization voltage characteristic curve. Through multi-level optimization calculation and real-time monitoring, the whole process management from balancing strategy formulation to execution effect evaluation is realized, especially in considering the temperature influence and polarization effect, strict constraints and compensation mechanisms are adopted to ensure the safety and effectiveness of the balancing process. Through the balancing control method based on multiple constraints, not only the requirements of efficiency and safety are balanced, but also the accuracy of control is improved through real-time monitoring and feedback mechanisms.

[0033] 106. Calculate the voltage deviation of the state of charge curve and the polarization voltage data to generate a voltage difference matrix of each single battery, and perform polarization compensation of the voltage difference on the voltage difference matrix to obtain a battery optimization result of the target battery pack.

[0034] In this embodiment, the state of charge curve of each monomer is divided into time periods and the change in charge per unit time is calculated to obtain state of charge gradient data, and the polarization voltage data of each monomer is accumulated during sampling periods and the fluctuation amplitude is extracted to obtain voltage fluctuation characteristic data, and the polarization response rate and monomer polarization strength are calculated on the state of charge gradient data to obtain a polarization characteristic matrix; the monomer polarization strength and response time are extracted from the polarization characteristic matrix to obtain the monomer polarization factor, and the associated difference calculation is performed on the monomer polarization factor and the voltage fluctuation characteristic data to generate a voltage difference matrix of each monomer battery; the monomer voltage compensation amount is calculated on the voltage difference matrix to obtain monomer voltage correction data, and the monomer voltage correction data is averaged voltage aligned to obtain the battery optimization result of the target battery pack.

[0035] In practical applications, based on the state of charge curve of each single battery, the entire balancing process is divided into fixed time intervals (usually 10 minutes), and the rate of change of charge per unit time is calculated in each time period. These rate of change data directly reflect the dynamic characteristics of the battery charging and discharging process, forming a complete state of charge gradient data set; at the same time, the recorded polarization voltage data are accumulated according to a fixed sampling period (usually 10ms), and the characteristic parameters of voltage fluctuation are extracted by analyzing the voltage fluctuation law and amplitude distribution characteristics. These parameters include the mean, variance and peak value of the fluctuation, which constitute the voltage fluctuation characteristic data; then, based on the state of charge gradient data, the polarization response rate of the battery in response to charging and discharging disturbances is calculated. This rate reflects the battery's adaptability to changes in charging and discharging currents, and the polarization strength of the single cell is calculated in combination with the amplitude of the voltage response. These two characteristic parameters together constitute a matrix that characterizes the polarization characteristics of the battery; then, through The polarization intensity coefficient and response time constant of each single cell in the polarization characteristic matrix are extracted. These parameters comprehensively reflect the polarization characteristics of the battery and form a single polarization factor. These polarization factors are correlated with the voltage fluctuation characteristic data obtained previously to calculate the difference between them. These difference values ​​directly reflect the voltage inconsistency between different cells, thereby constructing a complete voltage difference matrix; then based on the voltage difference matrix, the voltage compensation required for each single cell is calculated. This compensation amount needs to consider the polarization characteristics and safe working range of the battery to ensure that the compensated voltage does not exceed the safety boundary, and the correction data of the single cell voltage is obtained through these compensation calculations. Then, the voltage values ​​of all cells are aligned to the average voltage level of the battery pack through gradual adjustment. This alignment process needs to consider the rate limit of voltage adjustment to avoid damage to battery performance due to sharp voltage changes, thereby obtaining the battery optimization results of the target battery pack. Through multi-level data analysis and parameter optimization, comprehensive regulation from single cell characteristic analysis to group-level optimization is achieved. In particular, in dealing with polarization effects and voltage balance, precise mathematical models and strict control strategies are used to ensure the reliability and practicality of the optimization results. Not only does it take the dynamic characteristics of the battery into consideration, but it also improves the accuracy of control through real-time monitoring and feedback mechanisms, ultimately achieving efficient balancing and performance optimization of the battery pack.

[0036] In the embodiment of the present invention, the basic battery state characteristics are obtained by multi-source parameter acquisition and feature decoupling of each single cell in the target battery pack, and then the internal characteristics of the battery are obtained and the attenuation trend is decomposed through multi-band excitation scanning and impedance spectrum fitting to generate equivalent circuit model parameters; then state mapping and capacity calculation are performed based on the model parameters to obtain the performance evaluation results of the battery pack; then, through performance difference analysis and consistency calculation, the energy transfer network is constructed and the unbalanced propagation characteristics are identified; finally, temperature coupling calculation and compensation matrix reconstruction are performed based on the propagation characteristics to realize multi-channel parallel balancing control, and the optimization results of the battery pack are obtained through voltage deviation calculation and polarization compensation. Through hierarchical data and feature analysis, accurate control of battery pack balancing is achieved, especially in terms of charge state consistency, polarization characteristics and capacity balancing, the dynamic characteristics and unbalanced characteristics of the battery pack are fully considered, and the balancing effect is effectively improved; and the multi-level feature decoupling and state mapping strategy are adopted to realize the correlation analysis between battery parameters and enhance the reliability of balancing control; in addition, through propagation feature identification and compensation optimization, the balancing process is accurately controlled, thereby realizing efficient and accurate balancing of the battery pack as a whole.

[0037] The above describes the battery energy balanced distribution and optimization method in the embodiment of the present invention. The following describes the battery energy balanced distribution and optimization device in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a device for balancing and optimizing battery energy includes: The state separation module 201 is used to collect multi-source original parameter data of each single cell in the target battery pack, and extract multi-source features and separate single cell states from the multi-source original parameter data to obtain a single cell state data set; The trend decomposition module 202 is used to perform multi-band AC excitation scanning and impedance spectrum fitting on the single battery state data set to obtain internal characteristic parameters of the battery, and perform cycle aging feature recognition and capacity attenuation trend decomposition on the internal characteristic parameters of the battery to obtain battery equivalent circuit model parameters; The performance evaluation module 203 is used to perform model mapping and state decoupling on the battery state data set based on the battery equivalent circuit model parameters to obtain the battery state quantitative characteristics, and perform charge and discharge capacity integral calculation and cycle loss calculation on the battery state quantitative characteristics to obtain the performance evaluation result of the target battery pack; The path identification module 204 is used to calculate the performance difference and voltage consistency of the single cells based on the performance evaluation results to obtain single cell capacity distribution data, and to construct an energy transfer network and identify the propagation path between the single cells based on the battery pack imbalance parameter to obtain energy imbalance propagation characteristics; The parallel control module 205 is used to perform boundary calculation of multi-battery variable power and temperature coupling and reconstruction of the state of charge difference compensation matrix for the energy imbalance propagation characteristics, generate battery pack balancing parameters, and perform multi-channel parallel control on the target battery pack based on the battery pack balancing parameters to obtain the state of charge curve and polarization voltage data of each cell of the battery pack; The battery optimization module 206 is used to calculate the voltage deviation of the state of charge curve and the polarization voltage data to generate a voltage difference matrix of each single battery, and perform polarization compensation of the voltage difference on the voltage difference matrix to obtain the battery optimization result of the target battery pack.

[0038] In the embodiment of the present invention, the basic battery state characteristics are obtained by multi-source parameter acquisition and feature decoupling of each single cell in the target battery pack, and then the internal characteristics of the battery are obtained and the attenuation trend is decomposed through multi-band excitation scanning and impedance spectrum fitting to generate equivalent circuit model parameters; then state mapping and capacity calculation are performed based on the model parameters to obtain the performance evaluation results of the battery pack; then, through performance difference analysis and consistency calculation, the energy transfer network is constructed and the unbalanced propagation characteristics are identified; finally, temperature coupling calculation and compensation matrix reconstruction are performed based on the propagation characteristics to realize multi-channel parallel balancing control, and the optimization results of the battery pack are obtained through voltage deviation calculation and polarization compensation. Through hierarchical data and feature analysis, accurate control of battery pack balancing is achieved, especially in terms of charge state consistency, polarization characteristics and capacity balancing, the dynamic characteristics and unbalanced characteristics of the battery pack are fully considered, and the balancing effect is effectively improved; and the multi-level feature decoupling and state mapping strategy are adopted to realize the correlation analysis between battery parameters and enhance the reliability of balancing control; in addition, through propagation feature identification and compensation optimization, the balancing process is accurately controlled, thereby realizing efficient and accurate balancing of the battery pack as a whole.

[0039] above Figure 2 The battery energy balanced distribution and optimization device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The battery energy balanced distribution and optimization device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0040] Figure 3: is a structural schematic diagram of a battery energy balanced distribution and optimization device provided by an embodiment of the present invention. The battery energy balanced distribution and optimization device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage devices) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the battery energy balanced distribution and optimization device 300. Furthermore, the processor 310 may be configured to communicate with the storage medium 330 to execute a series of instruction operations in the storage medium 330 on the battery energy balanced distribution and optimization device 300.

[0041] The battery energy balancing and optimization device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 3 The structure of the battery energy balanced distribution and optimization device shown does not constitute a limitation on the battery energy balanced distribution and optimization device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0042] The present invention also provides a battery energy balanced distribution and optimization device, the computer device includes a memory and a processor, the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes each step of the battery energy balanced distribution and optimization method in the above-mentioned embodiments.

[0043] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes each step of the battery energy balanced distribution and optimization method.

[0044] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0045] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0046] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0047] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A battery energy balanced distribution and optimization method, characterized in that: The battery energy balanced distribution and optimization method comprises: Collecting multi-source original parameter data of each single cell in the target battery pack, and extracting multi-source features and separating single cell states from the multi-source original parameter data to obtain a single cell state data set; Performing multi-band AC excitation scanning and impedance spectrum fitting on the single battery state data set to obtain internal characteristic parameters of the battery, and performing cycle aging feature recognition and capacity attenuation trend decomposition on the internal characteristic parameters of the battery to obtain battery equivalent circuit model parameters; Based on the battery equivalent circuit model parameters, the battery state data set is model mapped and state decoupled to obtain a battery state quantitative feature, and the charge and discharge capacity integral calculation and cycle loss calculation are performed on the battery state quantitative feature to obtain a performance evaluation result of the target battery pack; The performance difference and voltage consistency of the single cells are calculated for the performance evaluation results to obtain single cell capacity distribution data, and the energy transfer network and propagation path identification between the single cells are performed for the battery pack imbalance parameter to obtain energy imbalance propagation characteristics; The energy imbalance propagation characteristics are subjected to boundary calculation of multi-battery variable power and temperature coupling and reconstruction of the state of charge difference compensation matrix to generate battery pack balancing parameters, and based on the battery pack balancing parameters, multi-channel parallel control is performed on the target battery pack to obtain the state of charge curve and polarization voltage data of each cell of the battery pack; The voltage deviation is calculated for the state of charge curve and the polarization voltage data to generate a voltage difference matrix for each single battery, and polarization compensation of the voltage difference is performed on the voltage difference matrix to obtain a battery optimization result for the target battery pack.

2. The battery energy balanced distribution and optimization method according to claim 1, characterized in that: The extracting of multi-source features and separation of single-cell states from the multi-source original parameter data to obtain a single-cell battery state data set includes: Extracting high-frequency components from the multi-source original parameter data to obtain high-frequency interference data, and performing frequency threshold truncation on the high-frequency interference data to obtain filtered multi-source original parameter data; Performing voltage curve segmentation on the filtered multi-source original parameter data to obtain charge-discharge conversion point data, and identifying the working state mode of the charge-discharge conversion point data to obtain working state mark data; Extracting the constant current interval of the working state mark data to obtain working polarization interval data, and performing step response calculation on the working polarization interval data to obtain a battery pack working characteristic curve; Capacity matching is performed on the battery pack operating characteristic curve to obtain single cell characteristic data, and the single cell characteristic data deviation coefficient is calibrated to obtain a single cell battery state data set.

3. The battery energy balanced distribution and optimization method according to claim 1, characterized in that: The method of performing multi-band AC excitation scanning and impedance spectrum fitting on the single cell state data set to obtain internal characteristic parameters of the battery, and performing cycle aging feature recognition and capacity attenuation trend decomposition on the internal characteristic parameters of the battery to obtain battery equivalent circuit model parameters includes: Extracting the operating cycle characteristics of the constant current charge and discharge interval corresponding to the single cell state data set to obtain cycle characteristic data; Extracting constant voltage interval and dynamic response characteristics of the cycle characteristic data to obtain charge and discharge time domain characteristic data, and performing pulse response decomposition and harmonic feature reconstruction on the charge and discharge time domain characteristic data to obtain charge and discharge frequency domain characteristic data; Performing AC impedance calculation and charge transfer impedance separation on the frequency domain characteristic data to obtain impedance characteristic data, and performing capacity loss calculation on the impedance characteristic data to obtain a battery performance attenuation curve; Performing life feature extraction on the performance decay curve to obtain battery decay feature data, and performing identification of a plurality of preset polarization parameters on the battery decay feature data to obtain battery polarization feature parameters; The battery polarization characteristic parameters are converted into battery internal resistance and equivalent capacitance to obtain initial circuit equivalent parameters, and the initial circuit equivalent parameters are calibrated in a circuit closed loop to obtain battery equivalent circuit model parameters.

4. The battery energy balanced distribution and optimization method according to claim 3, characterized in that: The method of performing model mapping and state decoupling on the battery state data set based on the battery equivalent circuit model parameters to obtain a battery state quantitative feature, and performing charge and discharge capacity integral calculation and cycle loss calculation on the battery state quantitative feature to obtain a performance evaluation result of the target battery pack includes: Separating polarization parameters of the battery equivalent circuit model parameters to obtain internal resistance characteristic data, and performing temperature coefficient compensation on the internal resistance characteristic data based on a temperature response coefficient corresponding to the target battery pack to obtain temperature compensation data; Mapping the working state characteristics of the temperature compensation data to obtain basic electrical parameters, and calculating the corresponding internal resistance change values ​​and polarization parameter change values ​​of the basic electrical parameters to obtain electrical characteristic change data; Identifying the charge and discharge cycle characteristics of the electrical characteristic change data to obtain cycle characteristic data, and dividing the discharge depth of the single cell and calculating the charge and discharge capacity ratio of the cycle characteristic data to obtain state of charge data; Performing capacity decay rate calculation and remaining life calculation on the state of charge data to obtain single cell health state data, and performing maximum discharge capacity calculation on the single cell health state data to obtain single cell actual capacity data; Based on the preset single cell capacity corresponding to the target battery pack, the actual capacity data of the single cells are compared for capacity difference to obtain battery pack capacity distribution characteristic data, and the capacity mean dispersion of the battery pack capacity distribution characteristic data is calculated to obtain the performance evaluation result of the target battery pack.

5. The battery energy balanced distribution and optimization method according to claim 1, characterized in that: The performance difference calculation and voltage consistency calculation of the single cell are performed on the performance evaluation results to obtain the single cell capacity distribution data, and the energy transfer network and propagation path identification between the single cells are performed on the battery pack imbalance parameter to obtain the energy imbalance propagation characteristics, including: Sorting the performance evaluation results of the battery pack by single cell capacity and calculating the capacity difference between adjacent single cells to obtain performance difference data, and grading the performance difference data by a preset capacity difference threshold to obtain capacity grade data; Mapping the three-dimensional single cell positions and performing capacity distribution statistics on the capacity level data to obtain single cell capacity distribution characteristic data, and calculating the voltage difference between single cells and pairing the nearest neighbor single cells on the single cell capacity distribution characteristic data to obtain network structure data between single cells; Identify the current flow direction and search for the shortest path of energy flow on the network structure data to obtain energy propagation path data, and identify the series-parallel connection relationship and calculate the energy transmission impedance on the propagation path data to obtain path weight data; The energy distribution and energy density of the single cell are calculated on the path weight data to obtain the battery energy space distribution data, and the balanced loop identification and transmission efficiency calculation are performed on the battery energy space distribution data to generate energy imbalance propagation characteristics.

6. The battery energy balanced distribution and optimization method according to claim 1, characterized in that: The energy imbalance propagation characteristics are subjected to boundary calculation of multi-battery variable power and temperature coupling and reconstruction of the state of charge difference compensation matrix to generate battery pack balancing parameters, and based on the battery pack balancing parameters, multi-channel parallel control is performed on the target battery pack to obtain the state of charge curve and polarization voltage data of each cell of the battery pack, including: The energy imbalance propagation characteristics are sorted by transmission paths and energy levels to obtain energy balance priority data, and the balance priority data are calculated by distances between single cells and normalized energy transmission path distances to obtain optimized single cell target data; Performing cell voltage difference calculation on the cell optimization target data to obtain voltage working range data, and performing battery working temperature boundary calculation on the voltage working range data to obtain temperature and voltage range data; Performing balancing time interval division and closed-loop path calculation on the temperature and voltage interval data to obtain multi-channel transmission path data, and performing energy transmission capacity calculation and minimum transmission loss sorting on the multi-channel transmission path data to generate battery pack balancing parameters; Performing multi-channel balancing current detection on each single cell of the battery pack balancing parameter to obtain single cell control current data, and performing single cell charge accumulation calculation on the single cell control current data to obtain single cell capacity change data; The single cell capacity change data is used to perform single cell charge state calculation and single cell polarization voltage calculation to obtain a charge state curve and polarization voltage data of each single cell of the battery pack.

7. The battery energy balanced distribution and optimization method according to claim 1, characterized in that: The voltage deviation calculation is performed on the state of charge curve and the polarization voltage data to generate a voltage difference matrix of each single battery, and polarization compensation of the voltage difference is performed on the voltage difference matrix to obtain the battery optimization result of the target battery pack, including: The state of charge curve of each monomer is divided into time periods and the change in charge per unit time is calculated to obtain state of charge gradient data, and the polarization voltage data of each monomer is accumulated by sampling periods and the fluctuation amplitude is extracted to obtain voltage fluctuation characteristic data, and the polarization response rate and monomer polarization intensity are calculated for the state of charge gradient data to obtain a polarization characteristic matrix; Extracting the monomer polarization intensity and response time of the polarization characteristic matrix to obtain the monomer polarization factor, and performing correlation difference calculation on the monomer polarization factor and the voltage fluctuation characteristic data to generate a voltage difference matrix of each monomer battery; The cell voltage compensation amount is calculated for the voltage difference matrix to obtain cell voltage correction data, and the average voltage is aligned for the cell voltage correction data to obtain the battery optimization result of the target battery pack.

8. A battery energy balanced distribution and optimization device, characterized in that: The battery energy balanced distribution and optimization device comprises: A state separation module is used to collect multi-source original parameter data of each single battery in the target battery pack, and extract multi-source features and separate single battery states from the multi-source original parameter data to obtain a single battery state data set; A trend decomposition module, used to perform multi-band AC excitation scanning and impedance spectrum fitting on the single battery state data set to obtain internal characteristic parameters of the battery, and perform cycle aging feature recognition and capacity attenuation trend decomposition on the internal characteristic parameters of the battery to obtain battery equivalent circuit model parameters; A performance evaluation module, configured to perform model mapping and state decoupling on the battery state data set based on the battery equivalent circuit model parameters to obtain a battery state quantification feature, and perform charge and discharge capacity integral calculation and cycle loss calculation on the battery state quantification feature to obtain a performance evaluation result of the target battery pack; A path identification module is used to calculate the performance difference and voltage consistency of the single cells based on the performance evaluation results to obtain single cell capacity distribution data, and to construct an energy transfer network and identify the propagation path between the single cells based on the battery pack imbalance parameter to obtain energy imbalance propagation characteristics; A parallel control module is used to perform boundary calculation of multi-battery variable power and temperature coupling and reconstruction of the state of charge difference compensation matrix for the energy imbalance propagation characteristics, generate battery pack balancing parameters, and perform multi-channel parallel control on the target battery pack based on the battery pack balancing parameters to obtain the state of charge curve and polarization voltage data of each cell of the battery pack; The battery optimization module is used to calculate the voltage deviation of the state of charge curve and the polarization voltage data, generate a voltage difference matrix of each single battery, and perform polarization compensation of the voltage difference on the voltage difference matrix to obtain the battery optimization result of the target battery pack.

9. A battery energy balancing and optimization device, characterized in that: The battery energy balancing distribution and optimization device comprises: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instruction in the memory to enable the battery energy balancing distribution and optimization device to perform each step of the battery energy balancing distribution and optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the battery energy balanced distribution and optimization method as described in any one of claims 1 to 7 are implemented.

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