An integrated power supply management method and system

Abstract

The application discloses an integrated power supply management method and system, relates to the related field of battery management, and comprises the following steps: acquiring a plurality of dynamic polarization resistance sets of a plurality of cell monomer sets, and generating three-dimensional temperature field space data of an energy storage cabin in a current sampling period; acquiring a plurality of real-time running currents by traversal; performing monomer heat generation power prediction to obtain a plurality of monomer predicted heat generation power value sets; performing space-time reconstruction deduction to generate a two-dimensional plane temperature prediction distribution matrix at a future preset time; identifying whether there is a target pixel point exceeding a preset safety temperature threshold in the direct-current energy storage cabin, predicting an over-temperature patch area in which local thermal stress aggregation occurs in the future, and performing power supply management on the direct-current energy storage cabin. The application solves the problem that the existing thermal management strategy cannot perform advanced identification and spatial differential control on the internal thermal state of the cell, and achieves the effect of performing advanced identification on the internal thermal risk of the cell and performing differential active control on the spatial heat distribution in the cabin.

Description

Technical Field

[0001] This application relates to the field of battery management, and in particular to an integrated power supply management method and system. Background Technology

[0002] With the rapid development of DC energy storage systems towards megawatt-hour-level high capacity, the total heat load generated by thousands of high-capacity cells in a single compartment during charging and discharging has increased dramatically. Thermal management has become a key constraint determining the safety and cycle life of energy storage systems. Currently, the industry generally adopts a passive thermal management strategy based on apparent temperature monitoring. This involves placing thermocouples or thermistors on the cell terminals or module surface and setting fixed temperature and voltage thresholds within the battery management system. When the monitored values ​​exceed the limits, a single protective action such as fan start / stop or branch circuit switching is triggered. However, this management method, which relies on triggering a response after apparent parameters exceed the limits, has certain problems: the heat accumulation rate inside a high-capacity cell is much higher than the temperature response rate of the casing, resulting in a significant time lag in surface temperature detection. At the same time, in actual engineering, the air duct layout or liquid cooling pipeline design inside the container inevitably creates local flow field dead zones, causing the above-mentioned lag effect to vary significantly in different spatial locations, making it difficult to effectively cover with a uniform threshold.

[0003] At present, the power supply thermal management strategy has the technical problem of being unable to identify the internal thermal state of the battery cell in advance and control it differently in different spaces. Summary of the Invention

[0004] This application provides an integrated power supply management method and system. By acquiring dynamic polarization internal resistance and three-dimensional temperature field data, and obtaining real-time operating current, it predicts the heat generation power of a single cell, and then combines the heat dissipation boundary conditions to deduce the future temperature distribution matrix, thereby identifying overheated patch areas and ultimately triggering power supply management actions. This solves the technical problem of existing power supply thermal management strategies being unable to proactively identify and spatially differentiate the thermal state inside the battery cell. It achieves the technical effect of proactively identifying thermal risks inside the battery cell and implementing differentiated proactive control of the thermal distribution in the cabin space.

[0005] This application provides an integrated power supply management method, comprising: acquiring multiple sets of dynamic polarization internal resistances of multiple battery cell sets through multiple online AC impedance monitoring chips deployed in multiple battery cluster branches of a DC energy storage compartment; simultaneously generating three-dimensional temperature field spatial data of the energy storage compartment for the current sampling period through a temperature sensor array deployed inside the energy storage compartment; acquiring multiple real-time operating currents corresponding to each of the multiple battery cluster branches; and predicting the heat generation power of each cell based on the real-time operating current corresponding to each battery cluster branch and the multiple sets of dynamic polarization internal resistances, thereby obtaining multiple sets of predicted heat generation power values ​​for each cell, wherein each predicted heat generation power value is used for... The system describes the predicted heat generation power of a single cell in a battery cluster branch within a preset future time period; based on the set of multiple predicted heat generation power values, the three-dimensional temperature field spatial data of the energy storage compartment, and the preset liquid cooling pipeline heat dissipation boundary conditions, it performs spatiotemporal reconstruction and deduction to generate a two-dimensional plane temperature prediction distribution matrix for a preset future time; based on the two-dimensional plane temperature prediction distribution matrix, it identifies whether there are target pixels exceeding a preset safe temperature threshold within the DC energy storage compartment, and predicts the overheated patch area where local thermal stress accumulation will occur in the future; in response to the overheated patch area corresponding to the identified target pixel, it manages the power supply to the DC energy storage compartment.

[0006] In a possible implementation, multiple online AC impedance monitoring chips deployed in multiple battery cluster branches of the DC energy storage compartment are used to obtain multiple sets of dynamic polarization internal resistances of multiple battery cell sets, and the following processing is performed: each battery cluster branch is equipped with a multiplexing selection array composed of multiple high-speed analog switches; multiple online AC impedance monitoring chips are connected to each corresponding battery cell in sequence through the corresponding multiplexing selection array at a preset polling period, so as to perform impedance demodulation and update of the currently connected battery cell in a time-division manner to obtain multiple sets of dynamic polarization internal resistances.

[0007] In a possible implementation, multiple online AC impedance monitoring chips sequentially connect to each corresponding battery cell via a corresponding multiplexing selection array at a preset polling period. This allows for time-division impedance demodulation and updating of the currently connected battery cell, obtaining multiple sets of dynamic polarization internal resistances. The following processes are then performed: injecting a weak sinusoidal AC excitation signal into the currently connected battery cell; demodulating and extracting the charge transfer internal resistance term and polarization overpotential voltage amplitude of the battery cell; linearly superimposing the charge transfer internal resistance term and the polarization overpotential voltage amplitude to update the dynamic polarization internal resistance; and after completing the preset polling period, constructing the sets of dynamic polarization internal resistances obtained from each battery cell to obtain the multiple sets of dynamic polarization internal resistances.

[0008] In a possible implementation, the heat generation power of a single cell is predicted based on the real-time operating current corresponding to each battery cluster branch and multiple sets of dynamic polarization internal resistances, resulting in multiple sets of predicted heat generation power values ​​for a single cell. The following processing is then performed: obtaining the ohmic internal resistance of each individual cell; summing the ohmic internal resistance of the cell with the corresponding dynamic polarization internal resistance from among the multiple dynamic polarization internal resistances to obtain multiple total internal resistance values ​​for a single cell; squaring the real-time operating current corresponding to each battery cluster branch and multiplying it by the corresponding total internal resistance value from among the multiple total internal resistance values ​​for a single cell to obtain multiple sets of predicted heat generation power values ​​for a single cell.

[0009] In a possible implementation, spatiotemporal reconstruction and deduction are performed based on the multiple sets of predicted heat generation power values ​​of individual units, the three-dimensional temperature field spatial data of the energy storage compartment, and the preset liquid cooling pipeline heat dissipation boundary conditions to generate a two-dimensional planar temperature prediction distribution matrix for a preset future time. The following processing is performed: Based on the physical arrangement structure of the entities within the DC energy storage compartment, the temperature data representing the height axis direction in the three-dimensional temperature field spatial data of the energy storage compartment is subjected to weighted average reduction and dimensionality reduction processing, and the three-dimensional mesh is mapped and compressed into an initial two-dimensional planar temperature prediction distribution matrix; the predicted heat generation power values ​​of each individual unit in the multiple sets of predicted heat generation power values ​​are converted into corresponding heat source temperature rise increments, and the heat source temperature rise increments are corrected for heat dissipation based on the preset liquid cooling pipeline heat dissipation boundary conditions, multiplied by a preset time extrapolation step size, and then superimposed onto the temperature reference obtained by the planar compression of the three-dimensional temperature field spatial data of the energy storage compartment, updating the values ​​of each pixel in the initial two-dimensional planar temperature prediction distribution matrix to obtain the two-dimensional planar temperature prediction distribution matrix.

[0010] In a possible implementation, the following processing is performed: the value of each pixel in the two-dimensional plane temperature prediction distribution matrix is ​​uniquely proportional to the predicted temperature, wherein the two-dimensional spatial pixel coordinates of the two-dimensional plane temperature prediction distribution matrix uniquely map the physical installation plane position of a specific series-parallel battery module in the DC energy storage compartment.

[0011] In a possible implementation, in response to the identified overheated patch area corresponding to the target pixel, power supply management is performed on the DC energy storage compartment, and the following processing is performed: read the target battery cluster branch number and target branch liquid cooling pipeline code corresponding to the target pixel in the overheated patch area; reduce the pulse width modulation duty cycle of the full-bridge switch tube of the bidirectional power converter connected to the target battery cluster branch number, and send a stepper motor drive stroke pulse signal to the electronically controlled regulating valve corresponding to the target branch liquid cooling pipeline code.

[0012] In a possible implementation, the pulse width modulation duty cycle of the full-bridge switch of the bidirectional power converter connected to the target battery cluster branch number is reduced, and the following processing is performed: a temperature deviation value is determined based on the predicted temperature corresponding to the target pixel and a preset safe temperature threshold; the magnitude of the target shunt current to be transferred is dynamically and inversely calculated based on the temperature deviation value; the pulse width modulation duty cycle of the full-bridge switch of the bidirectional power converter connected to the target battery cluster branch number is reduced according to a preset control ratio to increase the dynamic loop equivalent impedance of the target battery cluster branch, so that the bus current is physically shunt to the other parallel low-temperature battery cluster branches.

[0013] In one possible implementation, a stepper motor drive stroke pulse signal is sent to the electrically controlled regulating valve corresponding to the target branch liquid cooling pipeline code, and the following processing is performed: based on the predicted temperature rise slope of the overheated patch region, the refrigerant additional flow requirement value is determined; a number of stepper drive pulses proportional to the refrigerant additional flow requirement value and within a preset range are sent to the stepper motor drive chip corresponding to the target branch liquid cooling pipeline code, so as to increase the valve physical opening of the electrically controlled regulating valve corresponding to the target branch liquid cooling pipeline code in advance.

[0014] This application also provides an integrated power supply management system, including: an electrothermal parameter acquisition module, used to acquire multiple sets of dynamic polarization internal resistances of multiple battery cell sets through multiple online AC impedance monitoring chips deployed in multiple battery cluster branches of the DC energy storage compartment, and simultaneously generate three-dimensional temperature field spatial data of the energy storage compartment for the current sampling period through a temperature sensor array deployed inside the energy storage compartment; a current monitoring module, used to traverse and acquire multiple real-time operating currents corresponding to each of the multiple battery cluster branches; and a heat generation prediction module, used to predict the heat generation power of each cell based on the real-time operating current corresponding to each battery cluster branch and the multiple sets of dynamic polarization internal resistances, obtaining multiple sets of predicted heat generation power values ​​for each cell, wherein each predicted heat generation power value... The system is used to describe the predicted heat generation power of a single cell in a battery cluster branch within a preset time period in the future; the temperature field simulation module is used to perform spatiotemporal reconstruction simulation based on the set of multiple predicted heat generation power values ​​of single cells, the three-dimensional temperature field spatial data of the energy storage compartment, and the preset liquid cooling pipeline heat dissipation boundary conditions, to generate a two-dimensional plane temperature prediction distribution matrix for a preset future time; the patch identification module is used to identify whether there are target pixels exceeding a preset safe temperature threshold in the DC energy storage compartment based on the two-dimensional plane temperature prediction distribution matrix, and to predict the overheated patch area where local thermal stress accumulation will occur in the future; the power supply management module is used to manage the power supply of the DC energy storage compartment in response to the overheated patch area corresponding to the identified target pixel.

[0015] The proposed integrated power supply management method and system first acquires multiple sets of dynamic polarization internal resistances for multiple battery cells by using multiple online AC impedance monitoring chips deployed in multiple battery cluster branches within a DC energy storage chamber. Simultaneously, a three-dimensional temperature field spatial data of the energy storage chamber for the current sampling period is generated using a temperature sensor array deployed inside the energy storage chamber. Next, multiple real-time operating currents corresponding to each of the multiple battery cluster branches are acquired. Then, based on the real-time operating current corresponding to each battery cluster branch and the multiple sets of dynamic polarization internal resistances, the heat generation power of each cell is predicted, resulting in multiple sets of predicted heat generation power values ​​for each cell. The predicted heat generation power of each cell is... The value is used to describe the predicted heat generation power of a single cell in a battery cluster branch within a preset future time period. Then, based on the set of multiple predicted heat generation power values, the three-dimensional temperature field spatial data of the energy storage compartment, and preset liquid cooling pipeline heat dissipation boundary conditions, a spatiotemporal reconstruction simulation is performed to generate a two-dimensional planar temperature prediction distribution matrix for a preset future time. Based on this two-dimensional planar temperature prediction distribution matrix, the presence of target pixels exceeding a preset safe temperature threshold within the DC energy storage compartment is identified, and overheated patches with localized thermal stress accumulation are predicted. Finally, in response to the overheated patch regions corresponding to the identified target pixels, power supply management is implemented for the DC energy storage compartment. Through the above process, the method and system proposed in this application achieve the technical effect of proactively identifying internal thermal risks of battery cells and implementing differentiated proactive control of the thermal distribution within the compartment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a flowchart illustrating an integrated power supply management method provided in an embodiment of this application.

[0018] Figure 2 This is a bar chart comparing the heat generation power of a single battery cell in an embodiment of this application.

[0019] Figure 3 This is a schematic diagram of an integrated power supply management system provided in an embodiment of this application.

[0020] Figure labeling: 10 for electrothermal parameter acquisition module, 20 for current inspection module, 30 for heat generation prediction module, 40 for temperature field simulation module, 50 for patch identification module, and 60 for power supply control module. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0022] This application provides an integrated power supply management method, such as... Figure 1 As shown, the method includes: Step S100: Multiple online AC impedance monitoring chips deployed in multiple battery cluster branches of the DC energy storage compartment are used to obtain multiple sets of dynamic polarization internal resistance of multiple battery cell sets. At the same time, the three-dimensional temperature field spatial data of the energy storage compartment for the current sampling period is generated by the temperature sensor array deployed inside the energy storage compartment.

[0023] Specifically, this step is executed by the energy storage management controller. The DC energy storage compartment contains multiple battery cluster branches, each composed of multiple individual battery cells connected in series. Each battery cluster branch houses one or more online AC impedance monitoring chips. These chips are dedicated integrated circuits (ASICs) integrating a sine wave generator and quadrature demodulation circuitry, connected to the positive and negative terminals of the individual battery cells. The energy storage management controller reads the impedance demodulation results from each online AC impedance monitoring chip via its internal bus. Simultaneously, the controller reads the measurements from a temperature sensor array deployed within the energy storage compartment. This array consists of multiple digital temperature sensors fixed to the battery module surface, battery cluster support, and liquid cooling pipe inlets and outlets according to a preset spatial grid. Based on the physical three-dimensional coordinates of each temperature sensor and its current temperature reading, the controller uses a three-dimensional linear interpolation algorithm to generate three-dimensional temperature field spatial data covering the entire internal space of the energy storage compartment for the current sampling period. This three-dimensional temperature field spatial data is a three-dimensional grid data volume, with each grid node storing a temperature value. The grid coordinates correspond one-to-one with the physical spatial coordinates within the energy storage compartment. The current sampling period is the control cycle period of the energy storage management controller. For example, the period is 100ms.

[0024] In one possible implementation, multiple online AC impedance monitoring chips deployed within multiple battery cluster branches of a DC energy storage chamber are used to obtain multiple sets of dynamic polarization internal resistances for multiple sets of individual battery cells. Step S100 further includes step S110, where each battery cluster branch is equipped with a multiplexing selection array composed of multiple high-speed analog switches. The multiple online AC impedance monitoring chips sequentially access each corresponding individual battery cell through their respective multiplexing selection arrays at a preset polling period to perform time-division impedance demodulation updates on the currently accessed individual battery cells, thereby obtaining multiple sets of dynamic polarization internal resistances. Specifically, each battery cluster branch is equipped with a multiplexing selection array composed of multiple high-speed analog switches. The input terminals of each analog switch are connected to the positive and negative terminals of one individual battery cell within that battery cluster branch, and the output terminals of all analog switches are connected to the excitation signal output terminal and voltage measurement input terminal of an online AC impedance monitoring chip corresponding to that battery cluster branch. The preset polling period is configured by the energy storage management controller. For example, the polling period is 10 seconds, meaning that a complete poll of all individual cells within the battery cluster branch is completed every 10 seconds. Within one polling period, the energy storage management controller sends channel selection signals to the multiplexing selection array sequentially according to the serial number of the individual cells, allowing the online AC impedance monitoring chip to connect to each individual cell one by one. After completing impedance demodulation and updating on the currently connected cell, the energy storage management controller reads the dynamic polarization internal resistance value of that cell and stores it in its local cache, while simultaneously associating the dynamic polarization internal resistance value with the current cell number. After completing one preset polling period, the dynamic polarization internal resistance values ​​of all individual cells within the battery cluster branch are aggregated into a dynamic polarization internal resistance set. Each battery cluster branch independently and in parallel performs the above polling operation, thereby obtaining multiple dynamic polarization internal resistance sets corresponding to each of the multiple battery cluster branches. By using a multiplexing selection array, multiple battery cells can share a single online AC impedance monitoring chip in a time-sharing manner, which reduces the number of chips required and avoids signal crosstalk when multiple measurements are performed simultaneously, thereby improving measurement consistency.

[0025] In one possible implementation, multiple online AC impedance monitoring chips sequentially connect to corresponding individual battery cells via corresponding multiplexing selection arrays at a preset polling period. This allows for time-division impedance demodulation and updating of the currently connected battery cells, obtaining multiple sets of dynamically polarized internal resistances. Step S110 further includes step S111, injecting a weak sinusoidal AC excitation signal into the currently connected battery cell. Specifically, when the multiplexing selection array selects the current battery cell, a sinusoidal signal generator inside the online AC impedance monitoring chip generates a sinusoidal voltage signal with a fixed frequency and controlled amplitude. This sinusoidal voltage signal is applied to the positive and negative terminals of the currently connected battery cell via the currently active analog switch of the multiplexing selection array. The amplitude of the weak sinusoidal AC excitation signal is set to a value that does not significantly change the internal electrochemical state of the battery cell; for example, the amplitude is set to 10mV and the frequency is set to 10Hz. The online AC impedance monitoring chip also includes a DC blocking capacitor connected to the excitation signal output terminal to eliminate the influence of the battery cell's DC voltage on the excitation signal.

[0026] Step S112: Demodulate and extract the charge transfer internal resistance term and polarization overpotential voltage amplitude of the individual battery cell. Specifically, the online AC impedance monitoring chip acquires the response voltage signal of the individual battery cell under the action of a weak sinusoidal AC excitation signal through its internal voltage measurement input terminal. This response voltage signal is amplified by the preamplifier inside the chip and then sent to the quadrature demodulation circuit. The quadrature demodulation circuit uses a first reference signal with the same frequency and phase as the excitation signal and a second reference signal with a 90° phase lag to multiply the amplified response voltage signal, respectively. The multiplication result is then filtered by a low-pass filter to remove high-frequency components, obtaining the in-phase component and the quadrature component of the response voltage. The low-frequency contribution value of the in-phase component is divided by the effective value of the excitation current to obtain the charge transfer internal resistance term; the low-frequency contribution value of the quadrature component is the polarization overpotential voltage amplitude. The low-frequency component refers to the frequency component in the response signal that has the same frequency as the excitation signal, i.e., the 10Hz component.

[0027] Step S113: The charge transfer internal resistance term and the polarization overpotential voltage amplitude are linearly superimposed to update the dynamic polarization internal resistance. Specifically, the energy storage management controller reads the charge transfer internal resistance term value and the polarization overpotential voltage amplitude value output by the online AC impedance monitoring chip. The dynamic polarization internal resistance is calculated as follows: the resistance value of the charge transfer internal resistance term is used as the base value; the polarization overpotential voltage amplitude is divided by the terminal voltage of the cell measured at the current sampling time and then multiplied by a preset proportional conversion factor to obtain the equivalent resistance value of the polarization resistance; the base value and the equivalent resistance value are added to obtain the updated dynamic polarization internal resistance. The preset proportional conversion factor is determined by the calibration parameters of the online AC impedance monitoring chip; for example, the factor is set to 1. The updated dynamic polarization internal resistance replaces the dynamic polarization internal resistance value stored in the previous polling cycle for the cell.

[0028] Step S114: After completing the preset polling cycle in sequence, the dynamic polarization resistances of each individual cell are collected and constructed to obtain the multiple sets of dynamic polarization resistances. Specifically, the energy storage management controller arranges the dynamic polarization resistance values ​​of all individual cells updated in step S113 into a numerical sequence according to the cell numbering order within the battery cluster branch, and appends the battery cluster branch number and timestamp information to the sequence to form the dynamic polarization resistance set of the battery cluster branch. The dynamic polarization resistance sets of all battery cluster branches together constitute the multiple sets of dynamic polarization resistances, which are stored in the memory of the energy storage management controller.

[0029] Step S200: Iterate through and obtain multiple real-time operating currents corresponding to multiple battery cluster branches.

[0030] Specifically, the energy storage management controller collects the total bus current through a DC bus current sensor and simultaneously collects the real-time operating current of each battery cluster branch through branch current sensors. These branch current sensors are Hall effect current sensors, connected in series on the positive output line of each battery cluster branch. The energy storage management controller reads the analog current from each branch current sensor sequentially according to the battery cluster branch number, and obtains the digital real-time operating current value after analog-to-digital conversion. Since all individual cells within the same battery cluster branch are connected in series, this real-time operating current value is the common real-time operating current value for every individual cell within that branch. The energy storage management controller stores the multiple real-time operating current values ​​according to their corresponding battery cluster branch numbers.

[0031] Step S300: Based on the real-time operating current and multiple sets of dynamic polarization internal resistance corresponding to each battery cluster branch, the heat generation power of a single cell is predicted to obtain multiple sets of predicted heat generation power values ​​for a single cell. Each predicted heat generation power value is used to describe the predicted heat generation power value of a single cell in the battery cluster branch within a preset time period in the future.

[0032] Specifically, the energy storage management controller pairs the real-time operating current values ​​of each battery cluster branch obtained in step S200 with the dynamic polarization resistance set of the corresponding battery cluster branch obtained in step S100. For each individual cell, the square of the real-time operating current value of the battery cluster branch to which the cell belongs is used as a scaling factor, and the dynamic polarization resistance of the cell is used as a scaling coefficient. The product of the two is calculated to obtain the predicted heat generation power value of the cell. This predicted heat generation power value is used to characterize the heat power generated by the cell due to internal polarization effect in a future control cycle under the current operating current state. The energy storage management controller organizes the predicted heat generation power values ​​of all individual cells into multiple sets according to their respective battery cluster branches and series / parallel connection numbers. Each set corresponds to one battery cluster branch, and each element in the set corresponds to one individual cell.

[0033] In one possible implementation, the heat generation power of each cell is predicted based on the real-time operating current corresponding to each battery cluster branch and multiple sets of dynamic polarization internal resistances, resulting in multiple sets of predicted heat generation power values ​​for each cell. Step S300 further includes step S310, obtaining the ohmic internal resistance of each individual cell. Specifically, the energy storage management controller reads the pre-stored ohmic internal resistance calibration values ​​of each individual cell from its local non-volatile memory. These calibration values ​​are static parameters reflecting the ohmic resistance of the internal conductors of the cell, measured at standard ambient temperature using either DC discharge or AC impedance methods when the cell leaves the factory. These parameters can be updated offline during periodic maintenance as the cell ages. The ohmic internal resistance of each individual cell is stored in association with its unique number. The energy storage management controller queries the corresponding ohmic internal resistance value based on the target cell number currently being processed.

[0034] Step S320 involves summing the ohmic internal resistance of the battery cell with the corresponding dynamic polarization internal resistance from multiple dynamic polarization internal resistances to obtain multiple total internal resistance values ​​for each cell. Specifically, for each battery cell, the energy storage management controller extracts the ohmic internal resistance value of that cell from the ohmic internal resistance obtained in step S310, and then extracts the dynamic polarization internal resistance value of that cell from the set of dynamic polarization internal resistances of the battery cluster branch to which that cell belongs, obtained in step S100. The two internal resistance values ​​are then added together to obtain the total internal resistance value of that battery cell. This total internal resistance value represents the total equivalent DC internal resistance of the battery cell under the current polarization state.

[0035] Step S330: The real-time operating current corresponding to each battery cluster branch is squared and multiplied by the corresponding total internal resistance value of the multiple individual cells to obtain a set of multiple predicted heat generation power values ​​for each individual cell. Specifically, the energy storage management controller retrieves the real-time operating current value of the battery cluster branch where the target cell is located from step S200, performs a square operation on the real-time operating current value to obtain the squared current value. The total internal resistance value of the cell is retrieved from step S320. The squared current value is multiplied by the total internal resistance value to obtain the predicted heat generation power value of the cell. The energy storage management controller traverses all individual cells, repeats the above calculation process, and organizes all the calculated predicted heat generation power values ​​for each individual cell into multiple sets with the same index structure as the dynamic polarization internal resistance set in step S100, i.e., multiple sets of predicted heat generation power values ​​for each individual cell, according to the battery cluster branch number and the cell number.

[0036] For example, suppose the current real-time operating current of a certain battery cluster branch is 200A. This battery cluster branch contains four individual cells, numbered sequentially as No. 1, No. 2, No. 3, and No. 4. The energy storage management controller reads the ohmic internal resistances of each individual cell from step S310 as 0.5mΩ, 0.52mΩ, 0.48mΩ, and 0.51mΩ, respectively. The dynamic polarization internal resistances of each individual cell read from step S100 are 0.8mΩ, 0.9mΩ, 0.75mΩ, and 0.85mΩ, respectively. The energy storage management controller performs a summation operation to obtain the total internal resistance value of each cell, and then multiplies it by the square of the current value to obtain the predicted heat generation power value of each cell. Specific data are shown in Table 1 and... Figure 2 As shown.

[0037] Table 1

[0038] Step S400: Based on the set of predicted heat generation power values ​​of multiple individual units, the three-dimensional temperature field spatial data of the energy storage chamber, and the preset liquid cooling pipeline heat dissipation boundary conditions, perform spatiotemporal reconstruction and deduction to generate a two-dimensional planar temperature prediction distribution matrix for a preset future time.

[0039] Specifically, the energy storage management controller uses the set of multiple predicted heat generation power values ​​of individual cells obtained in step S330 as the heat source input, the three-dimensional temperature field spatial data of the energy storage compartment obtained in step S100 as the initial temperature state, and the preset liquid cooling pipeline heat dissipation boundary conditions as the heat dissipation constraint, to perform spatiotemporal reconstruction and deduction. The spatiotemporal reconstruction and deduction refers to the process of calculating the temperature distribution at a preset future time based on the current temperature distribution and heat source distribution, according to the principle of energy conservation. The preset liquid cooling pipeline heat dissipation boundary conditions include the inlet and outlet coolant temperatures of the liquid cooling pipeline, the coolant flow rate, and the contact heat transfer coefficient between the liquid cooling pipeline and the battery module; these parameters are pre-stored in the configuration file of the energy storage management controller. The energy storage management controller solves the discretized heat conduction equation, pushing forward the heat source, diffusion, and convection heat dissipation terms by a preset step in the time domain, ultimately generating a two-dimensional planar temperature prediction distribution matrix for the preset future time. Each element of the two-dimensional planar temperature prediction distribution matrix represents the predicted temperature value at a horizontal position within the energy storage compartment.

[0040] In one possible implementation, a spatiotemporal reconstruction and deduction is performed based on the set of predicted heat generation power values ​​of multiple individual cells, the three-dimensional temperature field spatial data of the energy storage compartment, and the preset liquid cooling pipeline heat dissipation boundary conditions to generate a two-dimensional planar temperature prediction distribution matrix for a preset future time. Step S400 further includes step S410, which, based on the physical arrangement structure within the DC energy storage compartment, performs weighted averaging and dimensionality reduction processing on the temperature data representing the height axis direction in the three-dimensional temperature field spatial data of the energy storage compartment, mapping and compressing the three-dimensional mesh into an initial two-dimensional planar temperature prediction distribution matrix. Specifically, the energy storage management controller reads the three-dimensional temperature field spatial data generated in step S100. This data is in the form of a three-dimensional mesh, with the three dimensions of the mesh corresponding to the length, width, and height coordinates of the energy storage compartment, respectively. The energy storage management controller determines the number of layers of the battery modules in the height direction based on the physical arrangement structure of the battery modules within the DC energy storage compartment. For each horizontal grid location consisting of the length and width directions, the energy storage management controller extracts all temperature data points along the height direction at that location. These temperature data points are then weighted and averaged according to their distance from the bottom of the battery module at each height level. Height levels closer to the bottom have a higher weight, and those farther away have a lower weight. The weighting coefficient is determined by the layered cooling efficiency of the liquid cooling piping arrangement; for example, the bottom layer has a weight of 50%, the middle layer has a weight of 30%, and the top layer has a weight of 20%. The weighted average yields a single temperature value, which serves as the initial two-dimensional temperature value for that horizontal grid location. The energy storage management controller performs the above weighted averaging operation on all horizontal grid locations, arranging all the initial two-dimensional temperature values ​​into a two-dimensional matrix according to the grid number in the length and width directions. This matrix is ​​the initial two-dimensional planar temperature prediction distribution matrix. The number of rows and columns in this matrix corresponds to the number of temperature sensor points in the length and width directions of the energy storage compartment, respectively.

[0041] Step S420: Convert the predicted heat generation power value of each cell in the set of multiple predicted heat generation power values ​​into the corresponding heat source temperature rise increment. After heat dissipation correction of the heat source temperature rise increment based on the preset liquid cooling pipeline heat dissipation boundary conditions, multiply it by a preset time extrapolation step size, and then superimpose it onto the temperature reference obtained by planar compression of the three-dimensional temperature field spatial data of the energy storage compartment. Update the values ​​of each pixel in the initial two-dimensional plane temperature prediction distribution matrix to obtain the two-dimensional plane temperature prediction distribution matrix. The value of each pixel in the two-dimensional plane temperature prediction distribution matrix is ​​uniquely proportional to the predicted temperature. The two-dimensional spatial pixel coordinates of the two-dimensional plane temperature prediction distribution matrix uniquely map the physical installation plane position of a specific series-parallel battery module in the DC energy storage compartment. Specifically, the energy storage management controller first obtains the predicted heat generation power value of each cell obtained in step S330. For each cell, divide its predicted heat generation power value by the product of its thermal capacity and mass to obtain the adiabatic temperature rise rate of the cell per unit time. The heat capacity-mass product is the mass of the individual battery cell multiplied by its specific heat capacity, a parameter pre-stored in the parameter table of the energy storage management controller. Then, the energy storage management controller calculates the heat dissipation power lost by the individual battery cell through the liquid cooling pipeline at the current temperature, based on the contact heat transfer coefficient and coolant temperature in the preset liquid cooling pipeline heat dissipation boundary conditions. The predicted heat generation power of the individual cell is subtracted from the heat dissipation power to obtain the net heat generation power. The net heat generation power is divided by the heat capacity-mass product to obtain the net temperature rise rate considering heat dissipation. The net temperature rise rate is multiplied by a preset time extrapolation step size to obtain the temperature rise increment of the individual battery cell over a preset time period. The preset time extrapolation step size is consistent with the control cycle of the energy storage management controller, exemplarily 100ms. The energy storage management controller determines the pixel position corresponding to each individual battery cell in the initial two-dimensional plane temperature prediction distribution matrix. This correspondence is pre-established and stored in a mapping table based on the physical mounting plane position of the individual battery cell within the battery module. The energy storage management controller adds the temperature rise increment of the corresponding single cell to the initial two-dimensional temperature value at each pixel location, and updates the temperature value at that pixel location. If a pixel location corresponds to the stacking location of multiple single cells, the average of the temperature rise increments of the multiple single cells is taken. After traversing all pixel locations and completing all update operations, the energy storage management controller obtains the final two-dimensional planar temperature prediction distribution matrix.

[0042] For example, suppose the initial two-dimensional temperature value of a certain pixel in the initial two-dimensional planar temperature prediction distribution matrix is ​​32.5℃, the mass of the corresponding battery cell is 5kg, and the specific heat capacity is 900J·kg. -1 ·℃ -1 Therefore, the product of heat capacity and mass is 4500 J·℃. -1The predicted heat generation power of this battery cell is 52W, and the contact heat transfer coefficient at this location in the preset liquid cooling pipeline heat dissipation boundary conditions is 20W·m. -2 ·℃ -1 The heat dissipation area is 0.1m². 2 The current surface temperature of the battery cell is 32.5℃, and the coolant temperature is 25℃. Therefore, the heat dissipation power is 20 × 0.1 × (32.5 - 25), which equals 15W. The net heat generation power is 52 - 15, which equals 37W. The net temperature rise rate is 37 ÷ 4500, approximately 0.00822℃·s. -1 With a preset time extrapolation step size of 10 seconds, the temperature rise increment is 0.00822 × 10, which equals 0.0822℃. The updated predicted temperature value for this pixel is 32.5 + 0.0822, which equals 32.5822℃. Specific data are shown in Table 2.

[0043] Table 2

[0044] Step S500: Based on the two-dimensional planar temperature prediction distribution matrix, identify whether there are target pixels in the DC energy storage chamber that exceed the preset safe temperature threshold, and predict the overheated patch area where local thermal stress accumulation will occur in the future.

[0045] Specifically, the energy storage management controller sets a preset safe temperature threshold, which is the maximum allowable temperature for normal operation of a single battery cell, exemplarily 45°C. The energy storage management controller iterates through the value of each pixel in the two-dimensional planar temperature prediction distribution matrix generated in step S420. The value of each pixel is compared with the preset safe temperature threshold. If the value of a pixel is greater than or equal to the preset safe temperature threshold, that pixel is marked as a target pixel. After traversing all pixels, the energy storage management controller performs connected component analysis on the marked target pixels. This connected component analysis uses a four-neighbor connectivity rule, meaning that horizontally and vertically adjacent target pixels belong to the same connected component. The area covered by each connected component is the predicted overheated patch area where localized thermal stress accumulation will occur in the future. If no target pixels exist, the current operating condition is deemed safe, and no overheated patch area will be generated.

[0046] In step S600, in response to the identified overheated patch area corresponding to the target pixel, power supply management is performed on the DC energy storage compartment.

[0047] Specifically, the energy storage management controller performs power management actions based on the overheated patch area identified in step S500. These actions include power derating adjustment and cooling flow rate adjustment. The energy storage management controller first determines the branch number of the battery cluster affected by the overheated patch area, then sends a power regulation command to the bidirectional power converter of the corresponding battery cluster branch, and simultaneously sends a flow rate regulation command to the liquid cooling piping system. This reduces the heat generation rate of the overheated area and increases its heat dissipation rate, thereby suppressing further deterioration of thermal stress accumulation.

[0048] In one possible implementation, in response to the identified overheated patch area corresponding to the target pixel, power supply management is performed on the DC energy storage compartment. Step S600 further includes step S610, reading the target battery cluster branch number and target branch liquid cooling pipeline code corresponding to the target pixel in the overheated patch area. Specifically, the energy storage management controller extracts the two-dimensional coordinates of all target pixels in the overheated patch area identified in step S500. For the two-dimensional coordinates of each target pixel, the energy storage management controller queries a pre-stored coordinate mapping table, which records the installation position of the physical battery module corresponding to each pixel coordinate, the battery cluster branch number to which it belongs, and the adjacent liquid cooling pipeline code. The energy storage management controller counts the battery cluster branch number that appears most frequently in the overheated patch area and determines this number as the target battery cluster branch number. At the same time, the energy storage management controller obtains the liquid cooling pipeline code corresponding to the target battery cluster branch, that is, the liquid cooling branch number responsible for cooling the battery cluster branch, and determines this number as the target branch liquid cooling pipeline code.

[0049] Step S620: Reduce the pulse width modulation duty cycle of the full-bridge switch of the bidirectional power converter connected to the target battery cluster branch number, and send a stepper motor drive stroke pulse signal to the electronically controlled regulating valve corresponding to the target branch liquid cooling pipeline code. Specifically, the energy storage management controller executes two parallel control actions. The first control action is to generate a duty cycle adjustment signal, which is used to reduce the pulse width modulation duty cycle of the full-bridge switch inside the bidirectional power converter connected to the target battery cluster branch number. The bidirectional power converter is an energy conversion device in the DC energy storage compartment that connects the battery cluster branch to the DC bus. The pulse width modulation duty cycle of its full-bridge switch determines the ratio between the output voltage of the battery cluster branch and the DC bus voltage, thereby affecting the output current of the battery cluster branch. The second control action is to generate a stepper motor drive stroke pulse signal and send the signal to the stepper motor drive chip of the electronically controlled regulating valve corresponding to the target branch liquid cooling pipeline code. The electrically controlled regulating valve is an electric ball valve or an electric regulating valve installed on the liquid cooling branch pipe. Its stepper motor driver chip receives pulse signals and drives the stepper motor to rotate, thereby changing the physical opening of the valve and regulating the coolant flow.

[0050] In one possible implementation, the pulse width modulation duty cycle of the full-bridge switch of the bidirectional power converter connected to the branch number of the target battery cluster is reduced. Step S620 further includes step S621, determining a temperature deviation value based on the predicted temperature corresponding to the target pixel and a preset safe temperature threshold. Specifically, the energy storage management controller selects the target pixel with the highest predicted temperature from the overheated patch region identified in step S500 as a representative point. The predicted temperature value of this representative point is subtracted from the preset safe temperature threshold, and the difference is taken as the temperature deviation value. This temperature deviation value reflects the severity of the current overheating; the larger the value, the more severe the thermal stress accumulation. For example, if the highest predicted temperature is 48.5°C and the preset safe temperature threshold is 45°C, then the temperature deviation value is 3.5°C.

[0051] Step S622: Based on the temperature deviation value, the target current to be diverted is dynamically calculated in reverse. Specifically, the calculation logic is divided into three-order recursive operations. First-order operation: Calculate the target power reduction. The energy storage management controller divides the temperature deviation value by a preset time extrapolation step size to obtain the target value of the temperature rise rate reduction required to eliminate the temperature deviation; then multiplies the target value of the required temperature rise rate reduction by the product of the heat capacity mass to obtain the target value of the net heat power reduction from the target cell. Second-order operation: Calculate the target current square value. The energy storage management controller obtains the total internal resistance value of the cell corresponding to the target pixel from step S320; divides the target value of the net heat power reduction by the total internal resistance value to obtain the ideal square value of the current of the cell under the target state. Third-order operation: Determine the target current size. The energy storage management controller subtracts the ideal square value of the current from the square value of the current in real time to obtain the square value of the current that needs to be reduced; the arithmetic square root of the square value of the current that needs to be reduced is the size of the target current to be diverted from the branch of the target battery cluster.

[0052] For example, suppose the current predicted temperature corresponding to a target pixel is 48.5℃, and the preset safe temperature threshold is 45℃, then the temperature deviation value is 3.5℃. The preset time extrapolation step size is 10s, and the product of heat capacity and mass is 4500J·℃. -1 The current real-time operating current is 200A, and the total internal resistance of a single cell is 1.30mΩ. Therefore, the first-order calculation shows that the target reduction in temperature rise rate is 3.5 ÷ 10, which equals 0.35℃·s. -1 The target value for reducing net heat power is 0.35 × 4500, which equals 1575 W. Second-order calculation: The ideal value of the squared current equals 1575 ÷ 0.0013, which equals 121.15 × 10⁻⁶. 4 A 2Third-order calculation: The square of the current is 200 × 200, which equals 40000A. 2 The required reduction in the square of the current is equal to 40000 - 121.15 × 10⁻¹⁰. 4 A 2 The negative result indicates that the current heat generation power of the individual battery cell far exceeds its heat dissipation capacity, and even reducing the current to zero cannot eliminate the temperature deviation within a single step. In this case, the energy storage management controller performs physical limiting processing, taking the upper limit of the square of the current to be reduced as the current square value, i.e., the current needs to be reduced to zero. Therefore, the target shunt current is equal to the current real-time operating current value, i.e., 200A. Specific data are shown in Table 3.

[0053] Table 3

[0054] Step S623: Reduce the pulse width modulation duty cycle of the full-bridge switch of the bidirectional power converter connected to the target battery cluster branch number according to a preset control ratio, so as to increase the dynamic loop equivalent impedance of the target battery cluster branch, and physically shunt the bus current to the other parallel low-temperature battery cluster branches. Specifically, the energy storage management controller performs reverse calculation and progressive adjustment of the duty cycle adjustment amount according to the shunt target current determined in step S622. Specifically, the energy storage management controller reads the dynamic loop equivalent impedance value of the target battery cluster branch at the current operating point from the local parameter table. The dynamic loop equivalent impedance value is equal to the sum of the total internal resistance of all cells in the branch and the line contact resistance. The energy storage management controller multiplies the shunt target current by the dynamic loop equivalent impedance value to obtain the voltage drop value that needs to be increased across the branch. The energy storage management controller obtains the current DC bus voltage value, divides the required voltage drop value by the current DC bus voltage value, and obtains the theoretical reduction ratio of the pulse width modulation duty cycle required to achieve the full shunt target. Because sudden changes in duty cycle can cause system current surges, the energy storage management controller executes a progressive adjustment strategy: it compares the theoretical reduction ratio with the preset maximum single adjustment ratio, and takes the smaller of the two as the actual duty cycle adjustment ratio for this operation; it then subtracts the actual duty cycle adjustment ratio from the current pulse width modulation duty cycle to obtain the target duty cycle for this operation. The energy storage management controller then writes the target duty cycle for this operation into the pulse width modulation register of the bidirectional power converter. After reducing the duty cycle, the output voltage of this battery cluster branch decreases. With the DC bus voltage constant, the output current of this branch decreases, and the reduced current is physically diverted to the remaining parallel low-temperature battery cluster branches, thereby achieving active transfer of heat source power.

[0055] For example, following the scenario of step S622. Assume the target shunt current determined in step S622 is 200A, the equivalent impedance of the dynamic loop of the target battery cluster branch is 1.5mΩ, the current DC bus voltage is 800V, the current pulse width modulation duty cycle is 80%, and the preset maximum single-time adjustment ratio is 5%. The energy storage management controller performs calculations: the required voltage drop is 200A × 0.0015Ω, which equals 0.30V; the theoretical reduction in duty cycle is 0.30V ÷ 800V, which equals 0.0375%. Since this theoretical reduction is much smaller than the preset maximum single-time adjustment ratio of 5%, the actual duty cycle adjustment ratio is taken as 0.0375%. The target duty cycle executed this time is 80% - 0.0375%, which equals 79.9625%. Specific data are shown in Table 4.

[0056] Table 4

[0057] In one possible implementation, step S620 further includes step S624, which involves sending a stepper motor drive stroke pulse signal to the electronically controlled regulating valve corresponding to the target branch liquid cooling pipeline code, to determine the refrigerant additional flow rate requirement based on the predicted temperature rise slope of the overheated patch region. Specifically, the energy storage management controller calculates the predicted temperature rise slope based on the predicted temperature rise trend of the target pixel in the overheated patch region identified in step S500. Specifically, the energy storage management controller stores the two-dimensional planar temperature prediction distribution matrix of the previous sampling period, subtracts the predicted temperature value of the target pixel in the overheated patch region obtained in the current sampling period from the predicted temperature value of the same pixel in the previous sampling period to obtain the temperature change; divides the temperature change by the time interval of the current sampling period to obtain the predicted temperature rise slope. The energy storage management controller multiplies the predicted temperature rise slope by a preset flow conversion coefficient to obtain the refrigerant additional flow rate requirement. The preset flow conversion coefficient is determined by the structural parameters of the liquid cooling pipeline and the thermophysical parameters of the coolant; for example, this coefficient is 0.5 L·min. -1 ·℃ -1 ·s.

[0058] Step S625: Send a number of stepper drive pulses proportional to the refrigerant replenishment flow requirement and within a preset frequency range to the stepper motor driver chip corresponding to the target branch liquid cooling pipeline code, so as to increase the physical valve opening of the electrically controlled regulating valve corresponding to the target branch liquid cooling pipeline code in advance. Specifically, the energy storage management controller converts the refrigerant replenishment flow requirement determined in step S624 into the pulse signal parameters required by the stepper motor driver chip. Specifically, it converts to: a preset correspondence between the valve opening of the electrically controlled regulating valve and the coolant flow rate, which is determined by the valve flow characteristic curve. The energy storage management controller looks up the target opening increment required to reach the refrigerant replenishment flow requirement at the current opening based on the curve. Divide the target opening increment by the opening change corresponding to the single-step angular displacement of the stepper motor to obtain the number of pulses to be sent. The energy storage management controller generates a number of stepper drive pulse signals equal to the number of pulses and within a preset frequency range. The preset frequency range is determined according to the rated frequency of the stepper motor, for example, 500Hz-2000Hz. The energy storage management controller sends the stepper drive pulse signal to the pulse input pin of the stepper motor driver chip corresponding to the target branch liquid cooling pipeline code, driving the stepper motor to rotate by the corresponding angle, thereby increasing the physical opening of the valve in advance, increasing the coolant flow rate, and actively reducing the temperature of the overheated patch area.

[0059] This application's embodiments solve the technical problem of existing power supply thermal management strategies being unable to proactively identify and spatially differentiate the thermal state of the battery cell. By acquiring dynamic polarization internal resistance and three-dimensional temperature field data, and obtaining real-time operating current, the heat generation power of a single cell is predicted. Then, by combining the heat dissipation boundary conditions to deduce the future temperature distribution matrix, the overheating patch area is identified, and finally, power supply management actions are triggered. This achieves the technical effect of proactively identifying thermal risks inside the battery cell and implementing differentiated proactive management of the thermal distribution in the cabin space.

[0060] In the above text, refer to Figures 1-2 An integrated power supply management method according to an embodiment of the present invention has been described in detail. Next, reference will be made to... Figure 3 An integrated power supply management system according to an embodiment of the present invention is described.

[0061] An integrated power supply management system according to an embodiment of the present invention addresses the technical problem of existing power supply thermal management strategies being unable to proactively identify and spatially differentiate the thermal state inside battery cells. It achieves the technical effect of proactively identifying thermal risks inside battery cells and implementing differentiated proactive control over the thermal distribution within the battery compartment. The integrated power supply management system includes: an electrothermal parameter acquisition module 10, a current monitoring module 20, a heat generation prediction module 30, a temperature field simulation module 40, a patch identification module 50, and a power supply control module 60.

[0062] The electrothermal parameter acquisition module 10 is used to acquire multiple sets of dynamic polarization internal resistances of multiple battery cell sets through multiple online AC impedance monitoring chips deployed in multiple battery cluster branches of the DC energy storage compartment. Simultaneously, it generates three-dimensional temperature field spatial data of the energy storage compartment for the current sampling period through a temperature sensor array deployed inside the energy storage compartment. The current monitoring module 20 is used to traverse and acquire multiple real-time operating currents corresponding to each of the multiple battery cluster branches. The heat generation prediction module 30 is used to predict the heat generation power of each cell based on the real-time operating current corresponding to each battery cluster branch and the multiple sets of dynamic polarization internal resistances, obtaining multiple sets of predicted heat generation power values ​​for each cell. Each predicted heat generation power value for a cell is used to describe a future preset time period. The system includes: a single cell in the internal battery cluster branch with a predicted heat generation power value; a temperature field simulation module 40, used to perform spatiotemporal reconstruction simulation based on the set of predicted heat generation power values ​​of multiple cells, the three-dimensional temperature field spatial data of the energy storage compartment, and the preset liquid cooling pipeline heat dissipation boundary conditions, to generate a two-dimensional plane temperature prediction distribution matrix for a preset future time; a patch identification module 50, used to identify whether there are target pixels exceeding a preset safe temperature threshold in the DC energy storage compartment based on the two-dimensional plane temperature prediction distribution matrix, and to predict the overheated patch area where local thermal stress accumulation will occur in the future; and a power supply management module 60, used to manage the power supply of the DC energy storage compartment in response to the overheated patch area corresponding to the identified target pixel.

[0063] The detailed description of the specific configuration of the electrothermal parameter acquisition module 10 is explained as follows: As mentioned above, multiple online AC impedance monitoring chips deployed in multiple battery cluster branches of the DC energy storage compartment are used to obtain multiple sets of dynamic polarization internal resistance of multiple battery cell sets. The electrothermal parameter acquisition module 10 may further include: an impedance demodulation and update unit used to sequentially access each corresponding battery cell through the corresponding multiplexing selection array with a preset polling period according to the multiple online AC impedance monitoring chips, so as to perform impedance demodulation and update of the currently accessed battery cell in a time-division manner to obtain multiple sets of dynamic polarization internal resistance. Each battery cluster branch is provided with a multiplexing selection array composed of multiple high-speed analog switches.

[0064] In this system, multiple online AC impedance monitoring chips sequentially connect to each corresponding battery cell via a corresponding multiplexing selection array at a preset polling cycle. This allows for time-division impedance demodulation and updating of the currently connected battery cell, resulting in multiple sets of dynamic polarization internal resistances. The impedance demodulation and updating unit may further include: an excitation signal injection subunit for injecting a weak sinusoidal AC excitation signal into the currently connected battery cell; an internal resistance-overpotential decoupling extraction subunit for demodulating and extracting the charge transfer internal resistance term and polarization overpotential voltage amplitude of the battery cell; a linear superposition subunit for linearly superimposing the charge transfer internal resistance term and the polarization overpotential voltage amplitude to update the dynamic polarization internal resistance; and a polling data aggregation subunit for sequentially completing the preset polling cycle and then constructing the sets of dynamic polarization internal resistances of each battery cell to obtain the multiple sets of dynamic polarization internal resistances.

[0065] The detailed description of the specific configuration of the heat generation prediction module 30 is explained as follows: As mentioned above, the heat generation power of a single cell is predicted based on the real-time operating current corresponding to each battery cluster branch and multiple sets of dynamic polarization internal resistances, thereby obtaining multiple sets of predicted heat generation power values ​​for a single cell. The heat generation prediction module 30 may further include: a cell ohmic internal resistance acquisition unit for acquiring the cell ohmic internal resistance of each single cell; a summation operation unit for summing the cell ohmic internal resistance with the corresponding dynamic polarization internal resistance among multiple dynamic polarization internal resistances to obtain multiple total internal resistance values ​​for a single cell; and a Joule heat generation calculation unit for squaring the real-time operating current corresponding to each battery cluster branch and multiplying it by the corresponding total internal resistance value among multiple total internal resistance values ​​for a single cell to obtain multiple sets of predicted heat generation power values ​​for a single cell.

[0066] The detailed description of the specific configuration of the temperature field extrapolation module 40 is as follows: As mentioned above, based on the multiple sets of predicted heat generation power values ​​of individual units, the three-dimensional temperature field spatial data of the energy storage cabin, and the preset liquid cooling pipeline heat dissipation boundary conditions, spatiotemporal reconstruction extrapolation is performed to generate a two-dimensional planar temperature prediction distribution matrix for a preset future time. The temperature field extrapolation module 40 may further include: a three-dimensional thermal field dimensionality reduction unit used to perform weighted average dimensionality reduction processing on the temperature data representing the height axis direction in the three-dimensional temperature field spatial data of the energy storage cabin based on the physical arrangement structure of the entities within the DC energy storage cabin, thereby reducing the dimensionality of the data. The three-dimensional mesh is mapped and compressed into an initial two-dimensional planar temperature prediction distribution matrix. The heat increment time-step extrapolation unit is used to convert each individual predicted heat generation power value in the set of multiple individual predicted heat generation power values ​​into the corresponding heat source temperature rise increment. After the heat source temperature rise increment is corrected for heat dissipation based on the preset liquid cooling pipeline heat dissipation boundary conditions, it is multiplied by a preset time extrapolation step size and then superimposed onto the temperature reference obtained by the planar compression of the three-dimensional temperature field spatial data of the energy storage chamber. The values ​​of each pixel in the initial two-dimensional planar temperature prediction distribution matrix are updated to obtain the two-dimensional planar temperature prediction distribution matrix.

[0067] The thermal increment time-step extrapolation unit may further include: the value of each pixel in the two-dimensional plane temperature prediction distribution matrix is ​​uniquely proportional to the predicted temperature, wherein the two-dimensional spatial pixel coordinates of the two-dimensional plane temperature prediction distribution matrix uniquely map the physical installation plane position of a specific series-parallel battery module in the DC energy storage compartment.

[0068] The power supply control module 60 is described in detail below: As mentioned above, in response to the identified overheated patch area corresponding to the target pixel, the power supply control module 60 can further include: a patch tracing and positioning unit for reading the target battery cluster branch number and target branch liquid cooling pipeline code corresponding to the target pixel in the overheated patch area; and an electro-thermal linkage execution unit for reducing the pulse width modulation duty cycle of the full-bridge switch tube of the bidirectional power converter connected to the target battery cluster branch number, and sending a stepper motor drive stroke pulse signal to the electronically controlled regulating valve corresponding to the target branch liquid cooling pipeline code.

[0069] Specifically, the electro-thermal linkage execution unit may further include: a temperature deviation calculation subunit for determining a temperature deviation value based on the predicted temperature corresponding to the target pixel and a preset safe temperature threshold; a shunt current inverse calculation subunit for dynamically and inversely solving for the required shunt target current based on the temperature deviation value; and an impedance adjustment shunt subunit for reducing the pulse width modulation duty cycle of the full-bridge switching transistor of the bidirectional power converter connected to the target battery cluster branch number according to a preset adjustment ratio, so as to increase the dynamic loop equivalent impedance of the target battery cluster branch and physically shunt the bus current to the other parallel low-temperature battery cluster branches.

[0070] The electro-thermal linkage execution unit may further include: a refrigerant addition calculation subunit for determining the refrigerant addition flow requirement value based on the predicted temperature rise slope of the overheated patch area; and a valve control pulse output subunit for sending a number of stepping drive pulses proportional to the refrigerant addition flow requirement value and with a frequency within a preset range to the stepping motor drive chip corresponding to the target branch liquid cooling pipeline code, so as to increase the physical valve opening of the electronically controlled regulating valve corresponding to the target branch liquid cooling pipeline code in advance.

[0071] The integrated power supply management system provided in this embodiment of the invention can execute the integrated power supply management method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0072] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An integrated power supply management method, characterized in that, The method includes: By deploying multiple online AC impedance monitoring chips in multiple battery cluster branches in the DC energy storage compartment, multiple sets of dynamic polarization internal resistance of multiple battery cell sets are obtained. At the same time, by deploying a temperature sensor array inside the energy storage compartment, three-dimensional temperature field spatial data of the energy storage compartment for the current sampling period is generated. Iterate through and obtain multiple real-time operating currents corresponding to each of the multiple battery cluster branches; Based on the real-time operating current and multiple sets of dynamic polarization internal resistance corresponding to each battery cluster branch, the heat generation power of a single cell is predicted, and multiple sets of predicted heat generation power values ​​of a single cell are obtained. Each predicted heat generation power value of a single cell is used to describe the predicted heat generation power value of a single cell in the battery cluster branch within a preset time period in the future. Based on the set of predicted heat generation power values ​​of multiple individual units, the three-dimensional temperature field spatial data of the energy storage chamber, and the preset liquid cooling pipeline heat dissipation boundary conditions, a spatiotemporal reconstruction simulation is performed to generate a two-dimensional planar temperature prediction distribution matrix for a preset future time. Based on the two-dimensional plane temperature prediction distribution matrix, the presence of target pixels exceeding the preset safe temperature threshold in the DC energy storage chamber is identified, and the overheated patch area where local thermal stress accumulation will occur in the future is predicted. In response to the identified overheated patch area corresponding to the target pixel, power supply management is performed on the DC energy storage compartment; The method involves acquiring multiple sets of dynamic polarization internal resistances for multiple battery cell sets through multiple online AC impedance monitoring chips deployed within multiple battery cluster branches in the DC energy storage compartment, including: Each battery cluster branch is equipped with a multiplex selection array consisting of multiple high-speed analog switches; Multiple online AC impedance monitoring chips are connected to each corresponding battery cell in sequence through a corresponding multiplexing selection array with a preset polling period, so as to perform impedance demodulation and update of the currently connected battery cell in a time-division manner to obtain multiple sets of dynamic polarization internal resistance. The multiple online AC impedance monitoring chips are sequentially connected to each corresponding battery cell via a corresponding multiplexing selection array at a preset polling period, so as to perform time-division impedance demodulation and update of the currently connected battery cell to obtain multiple sets of dynamic polarization internal resistances, including: A weak sinusoidal AC excitation signal is injected into the currently connected battery cell. Demodulate and extract the charge transfer internal resistance term and polarization overpotential voltage amplitude of the individual cell. The charge transfer internal resistance term is linearly superimposed with the polarization overpotential voltage amplitude to update the dynamic polarization internal resistance; After completing the preset polling cycle in sequence, the dynamic polarization internal resistance of each individual cell is collected and constructed to obtain the multiple sets of dynamic polarization internal resistance. The power supply management of the DC energy storage compartment in response to the identified overheated patch region corresponding to the target pixel includes: Read the target battery cluster branch number and target branch liquid cooling pipeline code corresponding to the target pixel in the overheated patch area; Reduce the pulse width modulation duty cycle of the full-bridge switch of the bidirectional power converter connected to the target battery cluster branch number, and send a stepper motor drive stroke pulse signal to the electronically controlled regulating valve corresponding to the target branch liquid cooling pipeline code; The reduction of the pulse width modulation duty cycle of the full-bridge switch of the bidirectional power converter connected to the branch number of the target battery cluster includes: The temperature deviation value is determined based on the predicted temperature corresponding to the target pixel and the preset safe temperature threshold. Based on the temperature deviation value, the magnitude of the target shunt current to be transferred is dynamically and inversely calculated. The pulse width modulation duty cycle of the full-bridge switch of the bidirectional power converter connected to the branch number of the target battery cluster is reduced according to the preset control ratio, so as to increase the dynamic loop equivalent impedance of the target battery cluster branch and physically divert the bus current to the other parallel low-temperature battery cluster branches. The stepper motor drive stroke pulse signal is sent to the electronically controlled regulating valve corresponding to the target branch liquid cooling pipeline code, including: Based on the predicted temperature rise slope of the overheated patch region, the required additional refrigerant flow rate is determined. Send a number of stepper drive pulses proportional to the refrigerant additional flow rate requirement and within a preset range to the stepper motor drive chip corresponding to the liquid cooling pipeline code of the target branch, so as to increase the physical opening of the electronically controlled regulating valve corresponding to the liquid cooling pipeline code of the target branch in advance.

2. The integrated power supply management method as described in claim 1, characterized in that, Based on the real-time operating current corresponding to each battery cluster branch and multiple sets of dynamic polarization internal resistances, the heat generation power of each cell is predicted, resulting in multiple sets of predicted heat generation power values ​​for each cell, including: Obtain the internal resistance of each individual battery cell in ohms; The ohmic internal resistance of the cell is summed with the corresponding dynamic polarization internal resistances among multiple dynamic polarization internal resistances to obtain the total internal resistance values ​​of multiple cells. After squaring the real-time operating current corresponding to each battery cluster branch, multiply it by the corresponding total internal resistance value of the individual cells to obtain a set of predicted heat generation power values ​​for multiple individual cells.

3. The integrated power supply management method as described in claim 1, characterized in that, Based on the set of predicted heat generation power values ​​of multiple individual units, the three-dimensional temperature field spatial data of the energy storage chamber, and the preset liquid cooling pipeline heat dissipation boundary conditions, a spatiotemporal reconstruction simulation is performed to generate a two-dimensional planar temperature prediction distribution matrix for a preset future time, including: Based on the physical arrangement structure of the DC energy storage chamber, the temperature data in the height axis direction of the three-dimensional temperature field spatial data of the energy storage chamber is subjected to weighted average reduction and dimensionality reduction processing, and the three-dimensional mesh is mapped and compressed into an initial two-dimensional plane temperature prediction distribution matrix. Each predicted heat generation power value in the set of multiple predicted heat generation power values ​​of individual cells is converted into the corresponding heat source temperature rise increment. After the heat source temperature rise increment is corrected for heat dissipation based on the preset liquid cooling pipeline heat dissipation boundary conditions, it is multiplied by a preset time extrapolation step size and then superimposed onto the temperature reference obtained by planar compression of the three-dimensional temperature field spatial data of the energy storage chamber. The values ​​of each pixel in the initial two-dimensional plane temperature prediction distribution matrix are updated to obtain the two-dimensional plane temperature prediction distribution matrix.

4. The integrated power supply management method as described in claim 3, characterized in that, The value of each pixel in the two-dimensional plane temperature prediction distribution matrix is ​​uniquely proportional to the predicted temperature. The two-dimensional spatial pixel coordinates of the two-dimensional plane temperature prediction distribution matrix uniquely map the physical installation plane position of a specific series-parallel battery module in the DC energy storage compartment.

5. An integrated power supply management system, characterized in that, The system is used to implement the integrated power supply management method according to any one of claims 1-4, the system comprising: The electrothermal parameter acquisition module is used to acquire multiple sets of dynamic polarization internal resistance of multiple battery cells by using multiple online AC impedance monitoring chips deployed in multiple battery cluster branches in the DC energy storage compartment. At the same time, it generates three-dimensional temperature field spatial data of the energy storage compartment for the current sampling period by using a temperature sensor array deployed inside the energy storage compartment. The current inspection module is used to traverse and obtain multiple real-time operating currents corresponding to multiple battery cluster branches; The heat generation prediction module is used to predict the heat generation power of a single cell based on the real-time operating current and multiple sets of dynamic polarization internal resistance corresponding to each battery cluster branch, and to obtain multiple sets of predicted heat generation power values ​​for a single cell. Each predicted heat generation power value is used to describe the predicted heat generation power value of a single cell in the battery cluster branch within a preset time period in the future. The temperature field extrapolation module is used to perform spatiotemporal reconstruction extrapolation based on the set of predicted heat generation power values ​​of the multiple units, the three-dimensional temperature field spatial data of the energy storage chamber, and the preset liquid cooling pipeline heat dissipation boundary conditions, and generate a two-dimensional planar temperature prediction distribution matrix for a preset future time. The patch recognition module is used to identify whether there are target pixels exceeding a preset safe temperature threshold in the DC energy storage chamber based on the two-dimensional plane temperature prediction distribution matrix, and to predict the overheated patch area where local thermal stress accumulation will occur in the future. The power supply management module is used to manage the power supply of the DC energy storage compartment in response to the overheated patch area corresponding to the identified target pixel.

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