Capacity adaptive coordination control system and method for distributed energy storage charging equipment

Abstract

The invention discloses a capacity adaptive coordination control system and method for distributed energy storage charging equipment, and relates to the technical field of charging safety control. The method is used for solving the problems of capacity sudden drop, vehicle charging delay and power grid interference caused by lithium precipitation. The method comprises the following steps: firstly, applying micro-current excitation in a charging pulse turn-off period by using a lithium precipitation characteristic monitoring module, extracting a phase deviation angle in a voltage relaxation curve, judging a lithium precipitation risk and generating a risk tag; then, the risk decision-making module calculates a charge migration proportionality coefficient and a floating coefficient in combination with the battery type and infrared thermal imaging information; then, the demand coupling module fuses the departure moment, the electric quantity gap and the power grid frequency fluctuation to generate a urgency degree weight and a pulse compensation parameter; and finally, the power reconstruction module completes charge migration and pulse sequence reconstruction according to the weight and the compensation parameter, triggers relaxation retest, and constructs a whole-flow dynamic coordination mechanism of the energy storage system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging safety control, in particular to a capacity adaptive coordination control system and method of distributed energy storage charging equipment. BACKGROUND

[0002] With the large-scale application of electric vehicles, the risk of lithium precipitation of batteries in fast charging scenarios has significantly increased. Lithium precipitation not only leads to capacity attenuation, but also may cause thermal runaway accidents. At the same time, high penetration rate of charging load aggravates the frequency fluctuation of power grid, and there is a strong coupling relationship between vehicle charging demand, power grid regulation demand and battery safety boundary. Distributed energy storage charging equipment needs to dynamically coordinate the urgency of vehicle charging and the stability demand of power grid under the premise of ensuring battery safety, which puts higher requirements on real-time risk decision and accurate power control.

[0003] Most of the existing charging control methods use state estimation mechanisms based on preset models or single physical indicators, which have limited accuracy in identifying battery aging state or lithium precipitation risk, especially in non-steady state charging phase or non-uniform battery pack internal, which is prone to identification lag and regulation error expansion. At the same time, when the battery capacity deteriorates or locally abnormals, some methods often deal with it by reducing the overall power or stopping charging, which cannot realize differentiated management based on risk level, resulting in a significant decrease in system charging efficiency. In addition, most charging systems use preset power grid frequency dead zone control, which cannot dynamically analyze the disturbance of harmonic components on the phase of charging pulse, resulting in the loss of synchronization between pulse sequence and power grid fluctuation, and aggravating frequency oscillation. The charge transfer between parallel batteries relies on static current sharing strategy, without considering the lithium precipitation risk gradient and health state difference of different batteries. The charge transfer process is easy to cause overcharge of receiving end battery, forming secondary safety hidden danger. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a capacity adaptive coordination control system and method of distributed energy storage charging equipment, which solves the problems in the above background art.

[0005] To achieve the above object, the present application is realized by the following technical solutions: the capacity adaptive coordination control system of the distributed energy storage charging equipment comprises the following modules: lithium precipitation characteristic monitoring module, risk decision module, demand coupling module, power reconstruction module; the lithium precipitation characteristic monitoring module is used for applying a micro-current excitation signal during the charging pulse off period, obtaining a voltage relaxation curve through high-frequency sampling, extracting a voltage recovery phase offset angle, determining a lithium precipitation risk level when the phase offset angle continuously increases and is accompanied by a secondary voltage rise, and outputting a risk label; the risk decision module is used for matching a lithium precipitation growth relationship curve according to the phase offset increment value in the risk label, combining the battery chemical type, generating a charge migration proportion coefficient, synchronously scanning infrared thermal imaging data to locate a local hotspot, and generating a migration proportion floating coefficient when the temperature difference of the hotspot area is significantly higher than that of the adjacent area; the demand coupling module is used for real-time access to the departure time stamp and the remaining battery capacity of the vehicle scheduling system, calculating the urgency weight value of the distance to the departure time, synchronously collecting the power grid frequency fluctuation waveform, and generating a pulse phase compensation parameter through harmonic analysis; the power reconstruction module is used for calculating the amount of charge to be transferred according to the charge migration proportion coefficient and the migration proportion floating coefficient, controlling the direct current converter to direct the charge from the high-risk battery to the healthy battery in parallel, distributing the charge receiving priority according to the urgency weight value, reconstructing the charging pulse sequence according to the pulse phase compensation parameter, and triggering the relaxation voltage retest instruction after the migration is completed.

[0006] Further, the specific process of applying a micro-current excitation signal during the charging pulse off period, obtaining a voltage relaxation curve through high-frequency sampling, and extracting a voltage recovery phase offset angle is as follows: a micro-current excitation of a specific amplitude is applied during the silent window period after the end of the charging pulse, and the voltage response waveform is recorded through high sampling frequency; the voltage change inflection point is extracted by differentiating the relaxation curve, the voltage change rate curvature extreme point is calculated, the capacitive component and the resistive component are separated through phase decoupling algorithm, and the phase offset difference value between the voltage recovery trajectory and the ideal exponential decay curve is calculated as the phase offset angle.

[0007] Further, when the phase offset angle continuously increases and is accompanied by a secondary voltage rise, the specific process of determining the lithium precipitation risk level and outputting the risk label is as follows: a continuous monitoring sequence of the phase offset angle is constructed, and when the phase offset angle increment in the adjacent multiple sampling periods exceeds the preset gradient threshold, the secondary voltage rise feature recognition is triggered; the non-monotonic decreasing inflection point of the relaxation curve is located through waveform second derivative analysis, the mutation point where the curvature changes from negative to positive is identified as the inflection point, and whether a continuous positive derivative interval appears after the inflection point is detected, if there is and the continuous time exceeds the set proportion of the relaxation period, it is determined that the secondary voltage rise feature exists; and a three-level risk label is generated according to the offset angle growth slope and the feature duration.

[0008] Further, according to the phase shift increment value in the risk label, combined with the lithium extraction growth relationship curve matching the battery chemistry type, the specific process of generating the charge migration proportion coefficient is as follows: retrieve the battery chemistry characteristic file to obtain the material reaction activity parameter, input the phase shift angle increment value into the preset parameter mapping table, and the mapping table is established based on the correlation between the phase shift increment of different chemical systems and the lithium extraction rate; adjust the migration proportion base through the increment change acceleration, calculate the temperature correction factor combined with the real-time temperature monitoring data, and output the migration proportion base and the temperature correction factor for comprehensive operation and processing as the charge migration proportion coefficient.

[0009] Further, the local hot spot is located by synchronously scanning the infrared thermal imaging data, and when the temperature difference of the hot spot area is significantly higher than that of the adjacent area, the specific process of generating the migration proportion floating coefficient is as follows: the infrared thermal imaging diagram is processed by grid partitioning, the average temperature difference of each unit and the eight adjacent areas is calculated, and the unit whose temperature difference continuously exceeds the set proportion of the average value of the adjacent area is marked as a candidate hot spot; the morphological dilation operation is performed on the candidate hot spot, the adjacent hot spots are merged to form a continuous area, the highest temperature of the hot spot area and the edge temperature gradient ratio are calculated, and when the gradient ratio continuously rises for multiple sampling periods, the migration proportion floating coefficient is generated according to the gradient ratio growth rate.

[0010] Further, the demand coupling module includes the following steps: real-time access to the departure time label data of the vehicle dispatching system, calculate the time remaining ratio from the current time to the departure time to generate a time urgency factor, synchronously obtain the state of charge value reported by the battery management system, calculate the gap between the current state of charge and the target value to generate an electric quantity gap factor, and weight and fuse the time urgency factor and the electric quantity gap factor to generate a comprehensive urgency weight; at the same time, the power grid voltage waveform signal is collected, the dominant harmonic component is extracted through fast spectrum analysis, and the compensation offset amount at the pulse trigger time is calculated according to the harmonic component amplitude proportion relationship.

[0011] Further, the specific process of calculating the amount of charge to be transferred according to the charge migration proportion coefficient and the migration proportion floating coefficient, and controlling the direct current converter to direct the charge from the high-risk battery to the parallel healthy battery is as follows: the migration proportion coefficient and the floating coefficient are convoluted to obtain the final migration proportion; the basic charge amount is calculated according to the product of the rated capacity value and the current state of charge value of the high-risk battery, and the actual migration charge amount is obtained by multiplying the basic charge amount by the final migration proportion; the direct current converter is controlled to establish a constant voltage output mode on the source battery side, the target voltage is set as the following value of the real-time voltage of the parallel healthy battery, and a constant current receiving mode is set on the target battery side, and the receiving current is set as the migration current value calculated dynamically according to the voltage difference.

[0012] Further, the specific process of assigning charge receiving priority according to the urgency weight value, reconstructing the charging pulse sequence according to the pulse phase compensation parameter, and triggering the relaxation voltage retest instruction after migration completion is as follows: according to the urgency weight value, the health battery group is sorted according to the receiving priority, and a charge distribution sequence is generated, and the total migration charge amount is divided into several charge distribution units according to the weight proportion; through the time division multiplexing mechanism, the charge distribution unit is transmitted to each target battery in turn, the pulse phase compensation parameter is analyzed to generate the device trigger time offset, the time bases of each charging device are aligned through clock synchronization, the pulse trigger time of each device is adjusted according to the offset, and a pulse sequence uniformly distributed in time dimension is formed; after the migration operation is completed, the relaxation voltage retest process is triggered: the test instruction containing the sampling frequency, excitation current parameter and trigger delay is sent to the battery management system.

[0013] The capacity adaptive coordination control method of the distributed energy storage charging device comprises the following steps: S1. A micro-current excitation signal is applied during the charging pulse off period, a voltage relaxation curve is obtained through high-frequency sampling, a phase offset angle is extracted, and when the phase offset angle continuously increases and is accompanied by a secondary voltage rise, the lithium extraction risk level is determined and a risk label is output; S2. According to the phase offset increment value in the risk label, the lithium extraction growth relationship curve is matched according to the battery chemical type, a charge migration proportion coefficient is generated, and local hot spots are located by synchronously scanning infrared thermal imaging data; when the temperature difference of the hot spot area is significantly higher than that of the adjacent area, a migration proportion floating coefficient is generated; S3. The vehicle dispatching system access time stamp and battery remaining power are accessed in real time, the urgency weight value is calculated, the power grid frequency fluctuation waveform is synchronously collected, and the pulse phase compensation parameter is generated through harmonic analysis; S4. The amount of charge to be transferred is calculated according to the charge migration proportion coefficient and the migration proportion floating coefficient, the direct current converter is controlled to direct the charge from the high-risk battery to the parallel healthy battery, the charge receiving priority is assigned according to the urgency weight value, the charging pulse sequence is reconstructed according to the pulse phase compensation parameter, and the relaxation voltage retest instruction is triggered after the migration is completed.

[0014] The present application has the following beneficial effects: (1) The capacity adaptive coordination control system of the distributed energy storage charging device realizes high-precision identification and active response to early lithium extraction risk through the synergistic effect of the lithium extraction feature monitoring module and the risk decision module. The system can break through the limitations of the traditional voltage monitoring on the lag response of hidden lithium extraction by injecting a micro-current excitation during the intermittent period of the charging pulse and analyzing the phase offset characteristics in the voltage relaxation process, and a forward-looking identification mechanism for the dynamic evolution process of lithium extraction is constructed. The risk decision module combines the battery material system and the lithium extraction growth relationship to dynamically generate a charge migration proportion coefficient, and realizes early warning of local thermal runaway by fusing infrared thermal imaging means, so that the migration strategy has material adaptability and thermal responsiveness, and the initiative and fineness of capacity regulation are improved.

[0015] (2) The capacity adaptive coordination control method of the distributed energy storage charging device integrates the demand coupling module and the power reconstruction module to establish a multi-source optimization mechanism for vehicle scheduling demand and power grid stability. The system can dynamically evaluate the charging urgency according to external scheduling information, and generate pulse compensation parameters for harmonic interference in combination with the power grid frequency disturbance, so as to balance the task driving and power quality stability. On this basis, the power reconstruction module realizes the directional migration of electric charge from high-risk monomers to healthy batteries, and effectively reduces the parallel load impact through the phase difference distribution strategy of the multi-channel pulse sequence. The voltage relaxation re-measurement mechanism builds a closed-loop verification process after the migration of electric charge, which substantially alleviates the risk of lithium precipitation, and provides strong support for the safety and energy efficiency coordination during the charging process.

[0016] Of course, implementing any product of the present application does not necessarily require all the advantages described above to be achieved simultaneously. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flow chart of the capacity adaptive coordination control system of the distributed energy storage charging device of the present application.

[0018] Figure 2 The flow chart of the capacity adaptive coordination control method of the distributed energy storage charging device of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application solve the problems of capacity drop, key vehicle charging delay and power grid harmonic disturbance diffusion caused by lithium precipitation in the distributed energy storage system through the capacity adaptive coordination control system and method of the distributed energy storage charging device. Lithium precipitation early warning is realized through real-time monitoring of the relaxation phase offset angle, and the risk positioning accuracy is improved through coupling analysis of infrared thermal imaging and chemical model. The power grid adaptability is enhanced through pulse phase compensation driven by harmonics. A dynamic electric charge migration mechanism based on urgency weight is designed to achieve collaborative optimization between safety and efficiency. The system integrates battery electrochemical characteristics, thermodynamic response, power grid interaction demand and other multidimensional constraints into a unified control framework, breaking through the limitations of traditional single-objective optimization.

[0020] The overall idea of the scheme in the embodiments of the present application is as follows: Firstly, the lithium precipitation characteristic monitoring module applies a micro-current excitation during the charging pulse off period, real-time acquires the voltage relaxation curve and extracts the phase offset feature, and constructs a lithium precipitation risk early identification model.

[0021] Subsequently, the risk decision module matches the corresponding lithium precipitation growth curve based on the identification result and the battery type, dynamically generates a migration proportion coefficient, and realizes thermal response adjustment of the migration strategy in combination with infrared thermal imaging information.

[0022] Then, the vehicle scheduling and power grid frequency information are introduced by the demand coupling module to form the task urgency weight and pulse compensation parameter, and the coupling coordination of the load side and the energy supply side is realized.

[0023] Finally, the power reconstruction module implements the charge directional migration and charging pulse reconstruction according to the above parameters, and verifies the lithium extraction relief effect through the relaxation retest mechanism after migration.

[0024] Please refer to Figure 1 The embodiment of the present application provides a technical scheme: a capacity adaptive coordination control system of a distributed energy storage charging device, comprising the following modules: a lithium extraction characteristic monitoring module, a risk decision module, a demand coupling module, and a power reconstruction module; the lithium extraction characteristic monitoring module is used to apply a micro-current excitation signal during the charging pulse shutdown period, obtain a voltage relaxation curve through high-frequency sampling, extract a phase offset angle of voltage recovery, determine a lithium extraction risk level when the phase offset angle continuously increases and is accompanied by a secondary voltage rise, and output a risk label; the risk decision module is used to match a lithium extraction growth relationship curve according to the phase offset increment value in the risk label in combination with the battery chemical type, generate a charge migration proportion coefficient, locate a local hotspot by synchronously scanning infrared thermal imaging data, and generate a migration proportion floating coefficient when the temperature difference of the hotspot area is significantly higher than that of the adjacent area; the demand coupling module is used to access the departure time stamp and the remaining battery capacity of a vehicle scheduling system in real time, calculate an urgency weight value from the departure time, synchronously collect a power grid frequency fluctuation waveform, generate a pulse phase compensation parameter through harmonic analysis; and the power reconstruction module is used to calculate the amount of charge to be transferred according to the charge migration proportion coefficient and the migration proportion floating coefficient, control the direct current converter to direct the charge from the high-risk battery to the healthy parallel battery, distribute the charge receiving priority according to the urgency weight value, reconstruct the charging pulse sequence according to the pulse phase compensation parameter, and trigger a relaxation voltage retest instruction after migration is completed.

[0025] In this embodiment, a lithium precipitation feature monitoring module is used to detect whether the battery has "lithium precipitation" phenomenon during charging, i.e. the deposition of metallic lithium on the surface of the negative electrode, which can cause short circuit and rapid capacity decay. This module applies a small current excitation during the intermittent period of the charging pulse (without affecting normal charging), collects the "voltage relaxation curve" (i.e. the trajectory of voltage change over time after current interruption), and extracts the "phase shift angle" (reflecting the delay characteristics of electrochemical process) through high-frequency sampling. When the phase continuously shifts and is accompanied by "secondary voltage rise" phenomenon (reflecting the active local lithium precipitation reaction), it is judged as a high-risk state, and a "risk label" is output as the basis for subsequent decision-making. Risk decision module, this module generates a "charge migration proportion coefficient" according to the "phase shift increment value" in the risk label (used to quantify the degree of risk change) and the "chemical type" of the battery (such as ternary lithium, lithium iron phosphate, etc.) against the pre-set "lithium precipitation growth relationship curve" (experimental lithium precipitation rate model), which is the recommended proportion of transferred charge. At the same time, the "infrared thermal imaging" technology is called to obtain the thermal distribution of the battery surface in real time, and if the temperature difference of a certain local area is much higher than the surrounding area, it is judged as a potential thermal runaway point, and the migration proportion is automatically increased to enhance the risk unloading ability of the area. Demand coupling module, this module accesses vehicle scheduling information such as departure time and remaining power, calculates the "urgency weight value" to reflect the urgency of the current vehicle for fast charging. At the same time, the power grid frequency waveform is collected, and the main frequency components (such as 5th and 7th harmonics) are extracted using "harmonic analysis" to generate "pulse phase compensation parameters", i.e. to modify the charging pulse time sequence to match the fluctuation characteristics of the power grid and improve system stability. Power reconstruction module, this module calculates the "amount of charge" to be transferred from the high-risk battery according to the aforementioned proportion coefficient and weight value, and controls the "direct current converter" to perform the migration process, i.e. to lead out the current from the high-risk battery and import it into the parallel healthy battery. The "charge receiving priority" is allocated according to the urgency to prioritize the charging needs of vehicles in urgent need. The "pulse phase compensation parameters" are used to adjust the timing of the charging pulse of each device to reduce the risk of harmonic superposition. After the migration is completed, the "relaxation voltage retest" is automatically triggered to verify whether the lithium precipitation risk has been effectively alleviated by re-collecting the phase shift angle.

[0026] Specifically, the specific process of applying a micro-current excitation signal during the off period of the charging pulse, obtaining the voltage relaxation curve through high-frequency sampling, and extracting the voltage recovery phase shift angle is as follows: a micro-current excitation signal of a certain amplitude is applied during the silent window period after the end of the charging pulse, and the voltage response waveform is recorded through high sampling frequency; the voltage change inflection point is extracted by differentiating the relaxation curve, the voltage change rate curvature extreme point is calculated, the capacitive component and the resistive component are separated by phase decoupling algorithm, and the phase shift angle is calculated as the phase shift difference between the voltage recovery trajectory and the ideal exponential decay curve.

[0027] In this embodiment, a small constant micro-current is applied to the battery during the silent period after the charge pulse is turned off (i.e., the window period without the influence of large current disturbance) , which is below the electrode polarization threshold to avoid introducing additional stress to the battery system. At the same time, the high-frequency sampling channel is turned on to record the voltage response under this micro-excitation , forming a complete voltage relaxation curve. This curve reflects the multi-physical field behavior of the electrode, electrolyte, and SEI film during the charge redistribution process. The voltage response differential and curvature extraction perform first and second derivative calculations on the voltage time series to obtain the voltage change rate and acceleration (curvature) . To eliminate high-frequency noise interference, a Savitzky-Golay filter is usually used for smoothing fitting. Voltage inflection point identification: define the local extreme point satisfies the following conditions: ; where represents the nonlinear inflection point of the voltage curve, usually corresponding to the transition time of charge transport behavior or the excitation time of interface reaction. Voltage component decoupling: capacitive and resistive separation Since the voltage response in the actual battery system is superimposed by multiple physical mechanisms, the intrinsic response path needs to be extracted by phase decoupling. Based on the impedance spectrum equivalent model, the total voltage response is split into a capacitive dominant component and a resistive dominant component , which are fitted as follows: capacitive component (exponential decay): ; resistive component (logarithmic hysteresis): ; nonlinear fitting and component separation are performed using the least mean square criterion, i.e., minimizing: ; parameter interpretation: : capacitive initial response amplitude; : equivalent capacitive time constant, reflecting the double-layer or SEI film dielectric recovery rate; : resistive response amplitude, related to Ohmic polarization and interface resistance change; : logarithmic response delay time, used to fit the interface buffer delay effect. Phase shift angle calculation constructs an ideal voltage decay trajectory , which is used as a reference signal to align with the measured voltage recovery trajectory and calculate the phase difference: ; this formula is based on the cosine similarity derivation, which represents the angle between the measured voltage and the theoretical trajectory in the waveform space, used to measure the dynamic response difference between the two. Parameter interpretation: : voltage recovery phase shift angle, reflecting the kinetic lag caused by side reactions such as lithium precipitation;: The upper limit of the silent period integral depends on the degree of system response decay; the voltage normalization within the integral interval is used to eliminate amplitude interference and only retain the phase information of the response trajectory shape.

[0028] Specifically, when the phase shift angle continues to increase and is accompanied by a secondary voltage rise, the specific process of determining the lithium analysis risk level and outputting the risk label is as follows: a phase shift angle continuous monitoring sequence is constructed, and when the phase shift angle increment in adjacent multiple sampling periods exceeds a preset gradient threshold, a secondary voltage rise feature recognition is triggered; a non-monotonic decreasing inflection point of the relaxation curve is located through waveform second derivative analysis, a mutation point where the curvature changes from negative to positive is identified as an inflection point, and whether a continuous positive derivative interval exists after the inflection point is detected; if it exists and the duration exceeds a set proportion of the relaxation period, it is determined that a secondary voltage rise feature exists; and a three-level risk label is generated according to the growth slope of the phase shift angle and the feature duration.

[0029] In the present embodiment, the phase shift angle continuous monitoring sequence construction and the increment gradient determination system take each pulse period as a time window, and record the voltage recovery phase shift angle sequence: ; wherein: : the phase shift angle measured in the first pulse off period; : the number of continuous detection periods. A first-order difference increment set is constructed for the sequence: ; the increment gradient threshold is set to ; when there are continuous periods that satisfy , it is determined that a phase continuous increase event exists, and the secondary voltage rise detection process is entered. The secondary voltage rise feature recognition and non-monotonic inflection point extraction obtain the voltage relaxation curve in the target period, take the relaxation time as the horizontal axis, and construct its second derivative curve: ; the numerical derivative of the discrete voltage sequence is estimated by high-order central difference method, and a point satisfying the following condition is found: ; that is, the curvature changes from negative to positive, indicating that the voltage change trend changes from concave downward to convex upward; after the inflection point , the first derivative is calculated, and if it satisfies: ; wherein, the proportion of the continuous positive derivative interval to the entire relaxation time satisfies: ; it is determined that the secondary voltage rise feature exists in the period. Parameter explanation: τ: relaxation time variable; V(τ): voltage value at the corresponding time point; τ*: curvature mutation inflection point time; ε: small domain distance, used to identify the mutation point; Δτ: continuous rising interval length after the inflection point; : full relaxation curve duration; : secondary rise time threshold proportion parameter, generally an empirical value. The risk label generation model (three-level classification) constructs a risk level criterion based on the offset angle growth slope and the positive guide duration for the cycle determined as "phase duration rise + secondary voltage rise": the slope estimation model constructs a local slope model based on Θn using the least squares linear fitting method: ; parameter explanation: : local slope of phase offset angle change, indicating the lithium extraction evolution speed; q: cycle sequence mean; θ: phase offset angle mean; the risk level determination matrix is constructed as follows: Parameter explanation: κ1, κ2: phase offset angle slope determination threshold; α1, α2: positive guide duration proportion determination threshold, the specific value is determined by sample statistics. The system generates a risk label according to the above combination results: "label 3": high risk, immediately trigger migration strategy; "label 2": medium risk, record and enter continuous monitoring area; "label 1": low risk, no intervention for the time being.

[0030] Specifically, according to the phase offset increment value in the risk label, the specific process of generating the charge migration proportion coefficient is as follows: the battery chemical property file is called to obtain the material reaction activity parameter, the phase offset angle increment value is input into the preset parameter mapping table, and the mapping table is established based on the correlation between the phase shift increment and the lithium extraction rate of different chemical systems; the migration proportion base is adjusted by the increment change acceleration, the temperature correction factor is calculated by combining the real-time temperature monitoring data, and the migration proportion base and the temperature correction factor are output for comprehensive operation and processing as the charge migration proportion coefficient.

[0031] In the embodiment, the battery chemical property file calling and parameter initialization system calls the corresponding chemical property file according to the battery type (ternary material, lithium iron phosphate), and obtains the following key reaction activity parameters: activity constant ζ: describes the material interface reaction kinetics; diffusion coefficient Dc: lithium ion diffusion rate in the material; reaction rate constant kr: interface lithium extraction reaction rate parameter. The above parameters provide a physical basis for subsequent mapping and calculation. The phase offset angle increment value mapping and lithium extraction rate relationship are established to define the current increment of the phase offset angle as: ; wherein: φ(m): the phase offset angle of the mth sample. Let the mapping function F(·) be based on the battery chemical type: ; Parameter explanation: Rs: corresponding lithium precipitation rate indicator, reflecting the lithium precipitation growth rate; Fψ: mapping function defined for chemical type ψ; Aψ, Bψ, Cψ: fitting coefficients dependent on battery chemistry, reflecting different material response characteristics; ψ: battery chemistry identifier. This formula establishes a nonlinear mapping relationship between phase increment and lithium precipitation rate, supporting differentiated material behavior simulation. The acceleration adjustment migration proportion base defines the acceleration of the phase offset angle increment change as follows: ; Wherein: : Sampling interval time. According to the acceleration adjustment migration proportion base : ; Parameter explanation: : Adjusted migration proportion base; : Basic migration proportion constant; : Acceleration adjustment sensitivity coefficient; : Adjustment parameter of control function slope; : Hyperbolic tangent function, used for nonlinear smooth adjustment. This step is used to capture the lithium precipitation acceleration process, dynamically adjust the migration base, and avoid excessive or insufficient migration. The temperature correction factor is calculated according to the real-time temperature monitoring value and the reference temperature , defining the temperature correction factor : ; Parameter explanation: : Temperature correction factor, reflecting the acceleration effect of temperature on lithium precipitation reaction; : Activation energy; : Gas constant; : Reference temperature; : Real-time monitoring temperature. This factor reflects the temperature sensitivity described by the Arrhenius equation, reflecting the influence of thermal excitation on lithium precipitation. The charge migration proportion coefficient is calculated comprehensively, and the charge migration proportion coefficient λ is calculated comprehensively from the migration proportion base and the temperature correction factor: ; This coefficient directly determines the allocation proportion of subsequent charge migration amount, realizing the coupled response of chemical characteristics and environmental temperature.

[0032] Specifically, the synchronous scanning infrared thermal imaging data locates the local hot spot, and when the temperature difference of the hot spot area is significantly higher than that of the adjacent area, the specific process of generating the migration proportion up-floating coefficient is as follows: The infrared thermal imaging diagram is subjected to grid partition processing, the average temperature difference of each unit and the eight adjacent domains is calculated, and the unit whose temperature difference continuously exceeds the set proportion of the average value of the adjacent domain is marked as a candidate hot spot; Perform morphological dilation operation on the candidate hot spot, merge adjacent hot spots to form a continuous area, calculate the highest temperature and edge temperature gradient ratio of the hot spot area, and when the gradient ratio continuously rises for multiple sampling periods, generate the migration proportion up-floating coefficient according to the gradient ratio growth rate.

[0033] In this embodiment, the infrared thermal image grid partitioning process and temperature difference calculation divide the infrared thermal image into multiple grid cells, and define the cell temperature as: ; wherein: : two-dimensional index of the grid cell, representing the th row and th column cell; : time sampling sequence index. Calculate the average temperature difference of the cell and its eight-neighborhood cells: ; parameter explanation: : average temperature difference of the cell and its neighborhood; : eight-neighborhood cell set of the cell ; parameter explanation: : number of eight-neighborhood cells, maximum 8. Candidate hotspot marking defines the average neighborhood temperature difference: ; wherein: : total number of grid rows and columns. Set the threshold proportion coefficient , when the condition: is met; and this condition is true in consecutive sampling periods, mark the cell as a candidate hotspot. Morphological dilation operation and hotspot region merging perform morphological dilation on the candidate hotspot binary image, and use the structure element to perform neighborhood expansion: ; parameter explanation: : candidate hotspot set; : morphological dilation operator; : structure element set, used to control the dilation range; : hotspot region set after dilation and merging. This step realizes the merging of adjacent hotspot cells, forming a continuous hotspot region. Hotspot region temperature gradient ratio calculation defines the highest temperature in the hotspot region as: ; the edge temperature is defined as the average value of the temperature of the grid cells on the boundary of the hotspot region: ; wherein: : set of boundary cells of the hotspot region; : number of edge cells. Define the temperature gradient ratio as: ; gradient ratio growth rate and migration proportion uplift coefficient generate the time-discrete growth rate of the gradient ratio: ; parameter explanation: : gradient ratio growth rate; : sampling time interval. When continuously increases in consecutive sampling periods, trigger the calculation of the migration proportion uplift coefficient , using nonlinear mapping: ; parameter explanation: p: migration proportion uplift coefficient; : maximum uplift amplitude adjustment coefficient;: Gradient growth response sensitivity parameter. This formula controls the gradual increase of the buoyancy coefficient by accumulating the gradient growth rate, ensuring that thermal anomaly regions receive higher migration priority.

[0034] Specifically, the demand coupling module includes the following steps: real-time access to the departure time marker data of the vehicle dispatching system, calculation of the remaining time percentage from the current time to the departure time to generate a time urgency factor, synchronous acquisition of the state of charge value reported by the battery management system, calculation of the difference between the current state of charge and the target value to generate a charge gap factor, and weighted fusion of the time urgency factor and the charge gap factor to generate a comprehensive urgency weight; simultaneously, acquisition of grid voltage waveform signals, extraction of dominant harmonic components through rapid spectrum analysis, and calculation of pulse trigger time compensation offset based on the harmonic component amplitude ratio.

[0035] In this implementation plan, the time urgency factor is calculated based on the departure time marker data that is accessed in real time through the vehicle dispatching system, and the current time is set as follows: The planned departure time is The preset reference time window is... Define the percentage of remaining time. : Parameter explanation: The remaining time percentage measures the urgency of charging time. Vehicle scheduled departure time; : The current moment; : Time window length, used to normalize the remaining time. Based on Design time urgency factor Nonlinear mapping function: Parameter explanation: Time pressure factor: the higher the value, the more urgent the situation. : Curve steepness adjustment coefficient, controls the slope of the function; Threshold bias parameter, controlling the midpoint of the function. This function ensures that the shorter the remaining time, the higher the urgency factor. The charge gap factor is calculated by receiving the current State of Charge (SOC) value reported by the Battery Management System (BMS). The target charging state is Define the energy gap factor. : Parameter explanation: The power shortage factor reflects the charging demand gap. Current battery SOC value; Target SOC value. The larger the gap, the higher the factor. The time urgency factor is generated by combining urgency weights. With the power shortage factor Weighted fusion: Parameter explanation: W: Overall urgency weight, comprehensively reflecting the priority of charging tasks; Time and energy weighting coefficients are used to adjust their relative importance. Cross-term coefficients reflect the coupling effect of the two factors. This expression considers both independent and cross-contributions of the two factors, achieving nonlinear weight calculation. Power grid harmonic component acquisition and compensation offset calculation: The power grid voltage waveform signal is acquired, and the spectrum is obtained using Fast Fourier Transform (FFT) to extract the dominant harmonic component information. Let the set of harmonic orders be... The corresponding amplitude is: Define the dominant harmonic components. satisfy: ; Calculate the compensation offset at the pulse trigger time : Parameter explanation: Compensation for phase offset; Harmonic weighting factor, reflecting the degree of influence of different harmonics on compensation; The ideal phase compensation angle corresponding to the harmonic components; :No. Second harmonic amplitude; Nonlinear adjustment index, which controls the influence of amplitude on weight.

[0036] Specifically, the process of controlling the DC-DC converter to directionally transfer charge from the high-risk battery to the parallel healthy battery, based on the charge transfer ratio coefficient and the transfer ratio levitation coefficient, is as follows: The final transfer ratio is obtained by convolving the transfer ratio coefficient and the levitation coefficient; the base charge is calculated based on the product of the rated capacity and the current state of charge of the high-risk battery; the base charge is multiplied by the final transfer ratio to obtain the actual transferred charge; the DC-DC converter is controlled to establish a constant voltage output mode on the source battery side, setting the target voltage to follow the real-time voltage of the parallel healthy battery; a constant current receiving mode is set on the target battery side, setting the receiving current to the transfer current value dynamically calculated based on the voltage difference.

[0037] In this implementation scheme, the final migration ratio calculation first involves convolving the previously obtained charge migration ratio coefficient λ with the migration ratio upscaling coefficient ρ to form the final migration ratio Λ. This introduces the time-series response adjustment characteristics of the migration strategy to thermal anomalies. The convolution operation is defined as follows: ; Parameter explanation: Λ(t): final migration ratio, representing the integrated migration coefficient at the current time; λ(θ): basic charge migration ratio coefficient, from the lithium analysis risk calculation; p(t, θ): time-reversed migration ratio floating coefficient, reflecting the influence of hot spot dynamic evolution; t: current system time variable. This convolution form can capture the dynamic coupling relationship between the thermal response delay characteristics and the migration strategy. The basic charge calculation is based on the rated capacity Qr of the high-risk battery and its real-time state of charge ζ to calculate the current migratable charge reference Qb: ; Parameter explanation: Qb: basic charge, representing the current actual charge of the high-risk battery; Qr: battery nominal rated capacity; ζ: current state of charge percentage, provided by the battery management system. The actual migration charge calculation multiplies the basic charge Qb by the final migration ratio Λ to obtain the actual migration charge : ; Parameter explanation: : actual migration charge, i.e. the total amount of charge to be transferred to the healthy battery; Λ: final migration ratio, containing risk and thermal response double regulation; Qb: basic charge. The charge directional migration control strategy migration control adopts a coordinated mode of "constant voltage source side + constant current target side": the source side constant voltage control sets the DC converter output voltage on the source battery side to , which needs to be dynamically consistent with the real-time voltage of the healthy battery: ; Parameter explanation: : source battery output voltage; : current real-time voltage of the healthy battery, used to set the reference voltage to ensure safe docking. Through the closed-loop voltage control module, the source side voltage is always in the target side safety window. The target side constant current control is on the parallel healthy battery side, adopting a constant current receiving mode. Set the voltage difference to , define the migration current as: ; Parameter explanation: : migration current, dynamic adjustment value; : current regulation factor, to prevent overcurrent; : voltage difference between the source battery and the healthy battery; : equivalent resistance of the connection line; : internal equivalent impedance of the healthy battery. Through the voltage difference driven constant current transmission, the high-risk battery charge is injected into the target healthy monomer in a controlled manner, avoiding overheating, overvoltage or impact.

[0038] Specifically, the process of assigning charge receiving priority based on urgency weight values, reconstructing the charging pulse sequence according to pulse phase compensation parameters, and triggering the relaxation voltage retest command after migration is as follows: The healthy battery packs are prioritized according to urgency weight values ​​to generate a charge allocation sequence. The total migrated charge is divided into several charge allocation units according to weight ratios. The charge allocation units are sequentially transmitted to each target battery via a time-division multiplexing mechanism. The pulse phase compensation parameters are parsed to generate device trigger time offsets. The time bases of each charging device are aligned using clock synchronization. The pulse trigger time of each device is adjusted according to the offset to form a pulse sequence uniformly distributed in the time dimension. After the migration operation is completed, the relaxation voltage retest process is triggered: a test command containing sampling frequency, excitation current parameters, and trigger delay is sent to the battery management system.

[0039] In this implementation scheme, the charge receiving priority ranking and charge allocation are first based on the urgency weight value corresponding to each healthy battery. The target batteries are sorted to form a charge receiving priority vector. Next, the total migrated load... The charge allocation for each target battery is determined by dividing the battery according to its weight. : Parameter explanation: Assigned to the first The charge of a healthy battery; Total moving load; : No. The urgency weight corresponding to each healthy battery; The total number of target healthy batteries. This weighted allocation strategy ensures that target batteries with higher task urgency receive a larger charge share first. Time-division multiplexing and pulse sequence modulation: Based on the charge allocation sequence, the system employs a time-division multiplexing (TDM) mechanism to divide each allocation unit... The signals are sequentially sent to different target batteries. To avoid pulse conflicts and suppress the effects of grid harmonic coupling, the system introduces pulse phase timing compensation offset. This is used to control the trigger timing of each device. The trigger timing offset is calculated using the following formula: Parameter explanation: : No. The pulse triggering time of each device; : Initial moment of the standard synchronous clock; The maximum allowed time offset configured in the system; : No. The pulse phase timing compensation offset of each device is derived from harmonic analysis. : Distributed phase perturbation for peak-shaving equalization to avoid spectral overlap. The above offset algorithm ensures the uniform distribution and phase offset of each charging device in the time domain, effectively reducing the risk of instantaneous power superposition. The trigger clock synchronization mechanism requires all devices to complete the local time base alignment operation before phase compensation. Through a high-precision time service module (such as IEEE1588 protocol), each charging controller synchronizes the local clock to the global master clock , ensuring that all time base systems are under a unified reference frame. The synchronization accuracy is controlled within microseconds, ensuring that the pulse trigger error is within the system tolerance range. After the migration is completed, the relaxation voltage retest procedure is automatically triggered to evaluate the lithium extraction relief effect. This step includes the following: generating a test instruction package containing the following parameters: sampling frequency : for setting the retest data acquisition rate; excitation current amplitude : for perturbation excitation; start delay time : set the delay time to ensure system thermal stability; command format: ; send the command package to the battery management system (BMS) through CAN communication, and the BMS controls the internal test logic to start, collects the relaxation voltage and feeds back to the host system for subsequent analysis and judgment.

[0040] Please refer to Figure 2 , the capacity adaptive coordination control method of distributed energy storage charging device, including the following steps: S1. Apply a micro-current excitation signal during the charging pulse off period, obtain the voltage relaxation curve through high-frequency sampling, extract the voltage recovery phase offset angle, and when the phase offset angle continuously increases and is accompanied by a secondary voltage rise, determine the lithium extraction risk level and output the risk label; S2. According to the phase offset increment value in the risk label, match the lithium extraction growth relationship curve combined with the battery chemical type to generate a charge migration proportionality coefficient, and simultaneously scan the infrared thermal imaging data to locate the local hot spot. When the temperature difference of the hot spot area is significantly higher than that of the adjacent area, generate a migration proportionality floating coefficient; S3. Real-time access to the departure time stamp and battery remaining capacity of the vehicle scheduling system, calculate the urgency weight value of the distance to the departure time, and simultaneously collect the power grid frequency fluctuation waveform to generate pulse phase compensation parameters through harmonic analysis; S4. Calculate the amount of charge to be transferred according to the charge migration proportionality coefficient and the migration proportionality floating coefficient, control the direct current converter to direct the charge from the high-risk battery to the parallel healthy battery, distribute the charge receiving priority according to the urgency weight value, reconstruct the charging pulse sequence according to the pulse phase compensation parameters, and trigger the relaxation voltage retest instruction after the migration is completed.

[0041] In this embodiment, step S1 introduces a voltage phase shift angle as a lithium precipitation precursor index, which can capture the relaxation curve disturbance caused by the change of electrode interface impedance outside the traditional charging voltage / current signal, which belongs to the fusion application of electrochemical impedance and phase response; through high-frequency sampling to extract the "second voltage rising feature", to identify the local side reaction caused by lithium dendrite micro-growth, and form the "non-monotonic voltage curve" detection logic; creatively fuse two kinds of signal trends (phase shift + voltage second derivative anomaly) to determine the risk level, and build an early warning mechanism, which breaks through the limitations of traditional BMS relying only on voltage and current windows. Step S2 maps the lithium precipitation risk index to the quantitative parameters (i.e. proportional coefficient) in the migration control strategy, solving the problem of action lag in the traditional "whether to switch" control method, and realizing precise migration control; the individual difference modeling of the battery chemical system (the lithium precipitation kinetics curves of different types of lithium batteries are different) is introduced, which makes the parameter mapping relationship more reliable and enhances the cross-platform universality; the thermal imaging data analysis is not directly used for "high temperature protection", but for the floating modulation factor of adjusting the migration proportion, which improves the response sensitivity and realizes the three-dimensional coupling mechanism of "electrochemistry-heat-control". Step S3 proposes to introduce vehicle task scheduling information into the energy storage charging control strategy, realizing "task-driven charge allocation", which is different from the traditional strategy of only looking at the power or SOC, and embodies the dynamic adaptation to the actual operation efficiency; the urgency weight value is modeled by "departure time + power gap" double factors, effectively balancing the contradiction between "time constraint" and "capacity shortage", and has a high adaptability of task priority sorting mechanism; the grid side frequency fluctuation data is introduced, and the phase compensation of the charging pulse is controlled through harmonic analysis, which is an important extension of the grid-friendly energy storage control, which helps to alleviate the harmonic pollution of the power grid caused by pulse charging. Step S4 proposes "charge directional migration", which realizes the active charge redistribution inside the parallel energy storage monomer, breaking through the previous solution of relying on passive balancing or passive stopping for the problem of uneven SOC of monomers; fusion of multi-factor migration parameters (risk level, thermal hotspot, task urgency, grid harmonic) forms a holographic control logic, so that each charge unit migration behavior has "directionality + targetness"; after migration is completed, a relaxation voltage retest mechanism is triggered, forming a "migration action-effect verification" closed loop, which is part of the system stability guarantee mechanism, and has important engineering practicality and long-term reliability significance.

[0042] In summary, the present application has at least the following effects: The capacity adaptive coordination control system and method of the distributed energy storage charging device realize early identification and dynamic adjustment of lithium precipitation risk, introduce a micro-current excitation in the charging pulse gap, extract voltage relaxation phase offset characteristics, construct a multi-dimensional risk label system, and significantly improve the sensing accuracy and response speed of potential lithium dendrite growth. A migration control mechanism based on the fusion of electrochemical characteristics and thermal imaging is established, which can adaptively generate a migration proportion factor according to the battery type and local hot spot conditions, realize accurate regulation of high-risk single cell charge, and improve the system capacity retention rate and safety. A scheduling model coupled with task driving and power grid sensing is introduced, which dynamically generates urgency weight and pulse compensation strategy combined with vehicle departure timing, battery remaining capacity and power grid frequency harmonic characteristics, and improves the response capability and grid compatibility of the energy storage system in the actual operation environment. A charge directional migration and pulse sequence reconstruction method for parallel battery systems is proposed, which realizes cross-cell energy redistribution through power reconstruction control logic, and forms a dynamically optimized charging pulse time sequence, thereby enhancing the internal balance of the system and the stability of external output. A migration verification closed loop is constructed, which automatically triggers the relaxation retest mechanism after the charge migration operation is completed, realizes quantitative feedback of lithium precipitation relief effect, and further guarantees the safety and controllability of the system in the long-term operation process.

[0043] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems, or computer program products. Accordingly, the application can be embodied in the form of complete hardware embodiments, complete software embodiments, or embodiments combining software and hardware aspects. Moreover, the application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) having computer usable program code embodied thereon.

[0044] The present application is described with reference to flowcharts and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The means for carrying out the functions specified in one or more flows and / or blocks. Figure 1 The means for carrying out the functions specified in one or more flows and / or blocks.

[0045] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0046] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0047] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those of skill in the art once they have the benefit of the present disclosure. Therefore, the appended claims are intended to encompass within their scope all possible variations and modifications of the preferred embodiments. 1

[0048] It is apparent that a person skilled in the art can make various changes and modifications to the application without departing from the spirit and scope thereof. Therefore, if these modifications and changes fall within the scope of the claims and their equivalents, it is intended to include them in the application.

Claims

1. A capacity self-adaptive coordinated control system of distributed energy storage charging equipment, characterized in that, The application relates to a lithium extraction feature monitoring module, a risk decision module, a demand coupling module and a power reconstruction module. The lithium extraction feature monitoring module is used for applying a micro-current excitation signal during a charging pulse shutdown period, obtaining a voltage relaxation curve through high-frequency sampling, extracting a voltage recovery phase offset angle, determining a lithium extraction risk level when the phase offset angle continuously increases and is accompanied by a secondary voltage rise, and outputting a risk label. The risk decision module is used for matching a lithium extraction growth relationship curve according to a phase offset increment value in the risk label, combining a battery chemical type, generating a charge migration proportion coefficient, synchronously scanning infrared thermal imaging data to locate a local hotspot, and generating a migration proportion floating coefficient when a temperature difference of the hotspot region is significantly higher than that of adjacent regions. The demand coupling module is used for real-time access to a vehicle dispatch system, a departure time stamp and a battery remaining power, calculating an urgency weight value of a distance from the departure time, synchronously collecting a power grid frequency fluctuation waveform, and generating a pulse phase compensation parameter through harmonic analysis. The power reconstruction module is used for calculating a charge amount to be transferred according to the charge migration proportion coefficient and the migration proportion floating coefficient, controlling a direct-current converter to direct the charge from a high-risk battery to a parallel healthy battery, distributing a charge receiving priority according to the urgency weight value, reconstructing a charging pulse sequence according to the pulse phase compensation parameter, and triggering a relaxation voltage retest instruction after the migration is completed. The specific process of applying a micro-current excitation signal during a charging pulse shutdown period, obtaining a voltage relaxation curve through high-frequency sampling, and extracting a voltage recovery phase offset angle is as follows:

2. The capacity self-adaptive coordinated control system of distributed energy storage charging device according to claim 1, characterized in that: A specific-amplitude micro-current excitation is applied during a silent window period after the end of the charging pulse, and a voltage response waveform is recorded through high sampling frequency. The specific process of applying a micro-current excitation signal during a charging pulse shutdown period, obtaining a voltage relaxation curve through high-frequency sampling, and extracting a voltage recovery phase offset angle is as follows: A specific-amplitude micro-current excitation is applied during a silent window period after the end of the charging pulse, and a voltage response waveform is recorded through high sampling frequency.

3. The capacity self-adaptive coordinated control system of distributed energy storage charging device according to claim 2, characterized in that: The specific process of applying a micro-current excitation signal during a charging pulse shutdown period, obtaining a voltage relaxation curve through high-frequency sampling, and extracting a voltage recovery phase offset angle is as follows: The specific process of determining a lithium extraction risk level and outputting a risk label when the phase offset angle continuously increases and is accompanied by a secondary voltage rise is as follows: A phase offset angle continuous monitoring sequence is constructed, and when the phase offset angle increment in adjacent multiple sampling periods exceeds a preset gradient threshold value, a secondary voltage rise feature recognition is triggered. A non-monotonic descending inflection point of the relaxation curve is located through waveform second-order derivative analysis, a mutation point where the curvature changes from negative to positive is recognized as the inflection point, and whether a continuous positive derivative interval exists after the inflection point is detected.

4. The capacity self-adaptive coordinated control system of distributed energy storage charging device according to claim 3, characterized in that: If the continuous positive derivative interval exists and the duration exceeds a set proportion of the relaxation period, the secondary voltage rise feature is determined. A three-level risk label is generated according to the offset angle growth slope and the feature duration. The specific process of matching a lithium extraction growth relationship curve according to a phase offset increment value in the risk label and combining a battery chemical type to generate a charge migration proportion coefficient is as follows: A battery chemical characteristic file is called to obtain material reaction activity parameters, and the phase offset angle increment value is input into a preset parameter mapping table. The mapping table is established based on the correlation between the phase offset increment and the lithium extraction rate of different chemical systems. The migration proportion base is adjusted by incremental change acceleration, the temperature correction factor is calculated by combining real-time temperature monitoring data, and the migration proportion base and the temperature correction factor are output for comprehensive operation and processing as the charge migration proportion coefficient.

5. The capacity self-adaptive coordinated control system of distributed energy storage charging device according to claim 4, characterized in that: The local hot spot is located by synchronous scanning infrared thermal imaging data, and when the temperature difference of the hot spot area is significantly higher than that of the adjacent area, the specific process of generating the migration proportion up-floating coefficient is as follows: The infrared thermal imaging image is grid partitioned, the average temperature difference of each unit and the eight adjacent areas is calculated, and the unit whose temperature difference continuously exceeds the set proportion of the average temperature difference of the adjacent area is marked as a candidate hot spot; The candidate hot spot is subjected to a morphological dilation operation, adjacent hot spots are merged to form a continuous area, the highest temperature of the hot spot area and the edge temperature gradient ratio are calculated, and when the gradient ratio continuously rises for multiple sampling periods, the migration proportion up-floating coefficient is generated according to the gradient ratio growth rate.

6. The capacity self-adaptive coordinated control system of distributed energy storage charging device according to claim 5, characterized in that: The demand coupling module includes the following steps: Real-time access to the departure time marker data of the vehicle dispatching system, calculate the remaining time proportion from the current time to the departure time to generate a time urgency factor, synchronously acquire the state of charge value reported by the battery management system, calculate the gap between the current state of charge and the target value to generate an electric quantity gap factor, and weight and fuse the time urgency factor and the electric quantity gap factor to generate a comprehensive urgency weight; At the same time, the power grid voltage waveform signal is collected, the dominant harmonic component is extracted through fast spectrum analysis, and the compensation offset amount at the pulse trigger time is calculated according to the harmonic component amplitude proportion relationship.

7. The capacity self-adaptive coordinated control system of distributed energy storage charging device according to claim 6, characterized in that: The specific process of calculating the amount of charge to be transferred according to the charge migration proportion coefficient and the migration proportion up-floating coefficient, and controlling the direct current converter to direct the charge from the high-risk battery to the parallel healthy battery is as follows: Convolution operation of the migration proportion coefficient and the up-floating coefficient to obtain the final migration proportion; According to the product of the rated capacity value and the current state of charge value of the high-risk battery, the basic charge amount is calculated, and the actual migration charge amount is obtained by multiplying the basic charge amount by the final migration proportion; The direct current converter is controlled to establish a constant voltage output mode on the source battery side, the target voltage is set as the following value of the real-time voltage of the parallel healthy battery, and a constant current receiving mode is set on the target battery side, and the receiving current is set as the migration current value calculated dynamically according to the voltage difference. 8.The system and method for adaptive coordination control of distributed energy storage charging device capacity according to claim 7, wherein: The specific process of distributing the charge receiving priority according to the urgency weight value, reconstructing the charging pulse sequence according to the pulse phase compensation parameter, and triggering the relaxation voltage retest instruction after the migration is completed is as follows: According to the urgency weight value, the receiving priority of the healthy battery pack is sorted, and a charge distribution sequence is generated, and the total migration charge amount is divided into several charge distribution units according to the weight proportion; Through the time division multiplexing mechanism, the charge distribution unit is transmitted to each target battery in turn, the pulse phase compensation parameter is analyzed to generate the device trigger time offset, the time bases of each charging device are aligned through clock synchronization, the pulse trigger time of each device is adjusted according to the offset amount, and a pulse sequence uniformly distributed in the time dimension is formed; After the migration operation is completed, the relaxation voltage retest process is triggered: a test instruction containing the sampling frequency, excitation current parameter and trigger delay is sent to the battery management system.

9. The capacity adaptive coordinated control method of the distributed energy storage charging device, applied to the capacity adaptive coordinated control system of the distributed energy storage charging device in any one of claims 1-8, characterized in that, The specific process includes the following steps: S1. Apply a micro-current excitation signal during the charge pulse off period, obtain the voltage relaxation curve through high-frequency sampling, extract the phase offset angle of voltage recovery, when the phase offset angle continuously increases and is accompanied by a secondary voltage rise, determine the risk level of lithium extraction and output the risk label; S2. According to the phase offset increment value in the risk label, combine the lithium extraction growth relationship curve matched with the battery chemical type to generate the charge migration proportion coefficient, and locate the local hot spot by synchronously scanning the infrared thermal imaging data, when the temperature difference of the hot spot area is significantly higher than that of the adjacent area, generate the migration proportion floating coefficient; S3. Real-time access to the departure time stamp and battery remaining capacity of the vehicle dispatching system, calculate the urgency weight value of the distance to the departure time, synchronously collect the power grid frequency fluctuation waveform, and generate the pulse phase compensation parameter through harmonic analysis; S4. Calculate the amount of charge to be transferred according to the charge migration proportion coefficient and the migration proportion floating coefficient, control the direct current converter to direct the charge from the high-risk battery to the parallel healthy battery, distribute the charge receiving priority according to the urgency weight value, reconstruct the charge pulse sequence according to the pulse phase compensation parameter, and trigger the relaxation voltage retest instruction after the migration is completed.

Images