Dynamic reconstruction topology system of liquid cooling charging module

Abstract

The invention discloses a dynamic reconstruction topology system of a liquid cooling charging module, which relates to the technical field of charging and comprises a battery state sensing module, a multi-objective optimization calculation module, a topology dynamic reconstruction module and a liquid cooling cooperative regulation and control module. The battery state sensing module collects key parameters such as the battery electric quantity, the health state and the temperature in real time, precision of a topological adjustment basis is achieved, multi-target collaborative optimization of the charging process is achieved through the multi-target optimization model constructed by the multi-target optimization calculation module, and the charging efficiency is improved. The topological structure is dynamically adjusted through the topological dynamic reconstruction module according to the state of the battery, heat dissipation is optimized in cooperation with the liquid cooling coordinated regulation and control module, the effects of improving charging efficiency, reducing energy loss, prolonging the service life of the battery and reducing the system cost are finally achieved, the charging time requirement of a user is met, meanwhile, charging safety, efficiency and economical efficiency are taken into consideration, and the charging cost is reduced. And the applicability and practicability of the liquid cooling charging module are improved.

Description

Technical Field

[0001] This invention relates to the field of charging technology, specifically to a dynamic reconfiguration topology system for a liquid-cooled charging module. Background Technology

[0002] A liquid-cooled charging module is a core unit of a charging device that uses liquid as a cooling medium. It mainly consists of a charging power unit, a liquid-cooled heat dissipation circuit, a control unit, and a detection unit. Compared to traditional air-cooled charging modules, it can more efficiently remove the heat generated by the power devices during charging by utilizing the circulating flow of coolant in the circuit. This allows it to support higher power charging demands and is currently widely used in scenarios with high requirements for charging power and heat dissipation efficiency, such as fast charging stations for electric vehicles and large-scale energy storage systems.

[0003] The dynamic reconfiguration topology system of a liquid-cooled charging module refers to a system that adjusts the internal power conversion topology of the liquid-cooled charging module (such as the conduction logic of switching devices and the connection methods between modules) in real time based on changes in battery state and actual usage requirements during charging, and collaboratively optimizes liquid cooling parameters. This system does not use a fixed topology; instead, it achieves precise matching between the topology and battery state and heat dissipation requirements through dynamic adjustment. It is the core support for the intelligent and efficient operation of liquid-cooled charging modules.

[0004] According to the invention patent application number 202510224527.0, a method, device, terminal, and storage medium for dynamic power allocation of a liquid-cooled charging pile are disclosed, belonging to the field of charging technology. The method includes: a main control board receiving the charging power demand of an electric vehicle connected to a corresponding charging terminal and issuing a start command to a power board; the power board allocating the charging modules connected to the charging terminal to the main control board; the power board calculating the allocated power and the demand power, and allocating idle charging modules according to the relationship between the allocated power and the demand power until the demand power is less than or equal to the allocated power; and again determining whether the demand power is less than the sum of the power of a single group of charging modules and the power of a preset buffer zone. If so, a group of allocated charging modules is removed; otherwise, no action is taken.

[0005] While the aforementioned solutions allow for flexible and dynamic power allocation to meet the charging needs of different vehicles, saving resources and costs, existing liquid-cooled charging modules still have certain shortcomings. Most existing liquid-cooled charging modules use a fixed topology structure, which cannot be dynamically adjusted according to battery status. When the battery health is poor, the charging parameters of the fixed topology can easily exacerbate battery damage. When the battery temperature is too high, the fixed topology cannot assist in heat dissipation through structural adjustments, posing safety hazards. Existing technologies often focus only on a single objective, failing to construct a multi-objective optimization model that includes charging efficiency, charging time, battery life, and system cost. This makes it impossible to simultaneously meet user charging time requirements while extending battery life and reducing system costs, resulting in insufficient practicality. Furthermore, existing liquid-cooled charging modules lack sufficient accuracy in monitoring battery status, often only monitoring battery charge level without using key parameters such as battery health and real-time temperature as the basis for topology adjustment. This leads to a lack of precision in topology adjustment, making it difficult to achieve comprehensive optimization of the charging process. Therefore, developing a dynamic reconfiguration topology system for liquid-cooled charging modules is of great significance. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a dynamic reconfiguration topology system for liquid-cooled charging modules. This system can monitor key parameters such as battery charge, health status, and temperature in real time, and construct a multi-objective optimization model that includes charging efficiency, charging time, battery life, and system cost. Based on different battery states, it intelligently reconfigures the charging module topology and optimizes charging parameters. When the battery health is poor, a mild mode is selected to reduce damage; when the temperature is too high, it switches to a topology with better heat dissipation to ensure safety. Ultimately, it achieves comprehensive optimization of improved charging efficiency, reduced energy loss, extended battery life, and reduced system cost. This effectively solves the problems of poor adaptability and inability to balance multiple objective requirements of existing liquid-cooled charging modules, and is suitable for charging scenarios of various electric vehicles and energy storage devices.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a dynamic reconfiguration topology system for a liquid-cooled charging module, the system comprising: a battery state sensing module, a multi-objective optimization calculation module, a topology dynamic reconfiguration module, and a liquid-cooled collaborative control module; The battery status sensing module integrates a voltage detection component, a current detection component, and a temperature detection component. It is used to collect the battery's power, health status, battery surface temperature, and battery internal temperature parameters in real time, and convert the collected analog signals into digital signals and transmit them to the multi-objective optimization calculation module. The multi-objective optimization calculation module is based on the parameters transmitted by the battery state sensing module and takes meeting the user's preset charging time requirements as a constraint. It constructs a multi-objective optimization model that covers maximizing charging efficiency, ensuring that the charging time does not exceed the set range, minimizing battery cycle loss, and minimizing the sum of module energy consumption and maintenance costs. After processing by the optimization algorithm, it outputs the optimal charging parameters and topology structure requirement signal to the topology dynamic reconstruction module. The topology dynamic reconstruction module pre-stores topology schemes adapted to different battery states. After receiving the topology requirement signal, it adjusts the topology connection mode by controlling the on and off logic of the internal switching devices of the topology, completes the topology reconstruction, and synchronously feeds back the topology state to the liquid cooling collaborative control module. The liquid cooling coordinated control module adjusts the operating parameters of the liquid cooling heat dissipation circuit according to the topology state, so as to realize the coordinated optimization of topology reconstruction and liquid cooling heat dissipation.

[0008] Furthermore, the voltage detection component in the battery state sensing module uses a high-precision voltage sensor, the current detection component uses a Hall current sensor, and the temperature detection component includes a contact temperature sensor and a non-contact infrared temperature sensor. The voltage detection component is used to collect the battery's terminal voltage, the current detection component is used to collect the battery's charging and discharging current, the contact temperature sensor is used to collect the battery's surface temperature, and the non-contact infrared temperature sensor is used to collect the battery's internal temperature. The battery state sensing module calculates the battery's state of health (SOH) using the following formula: In the formula, This represents the current actual capacity of the battery. For the battery's rated capacity, The current internal resistance of the battery. The initial internal resistance of the battery is given by the coefficient. and These are the weighting coefficients, and , and Through experiments, samples of the same model of battery were selected, and the impact of capacity decay and internal resistance change on battery performance was tested under standard charge and discharge conditions at different cycles. The specific values ​​were determined through linear regression analysis.

[0009] Furthermore, the optimization algorithm in the multi-objective optimization calculation module is a weighted summation method. This module has a built-in weight adjustment unit, which can receive the weight coefficients of each optimization objective manually input by the user through the user interface, or automatically generate the weight coefficients of each optimization objective based on the characteristics of the charging scenario, according to the actual application scenario. The multi-objective optimization calculation module calculates the comprehensive value of multi-objective optimization using a formula. The formula is: ,in, This is a weighting coefficient for charging efficiency. This is a weighting factor for charging time. This is a weighting factor for battery life. This is the system cost weighting coefficient, and This represents the actual charging efficiency. Preset charging time for users. Actual charging time This represents the theoretical maximum cycle life of the battery. This refers to the actual cycle life of the battery. The theoretical maximum cost threshold of the system. For the actual system cost, in both fast charging scenarios for electric vehicles and charging scenarios for energy storage systems, the weight adjustment unit sets different weights based on the priority requirements of each scenario for charging efficiency, charging time, battery life, and system cost. , , , The specific value.

[0010] Furthermore, the topology structure schemes pre-stored in the topology dynamic reconstruction module include three types: high-efficiency topology suitable for scenarios where the battery health is good and the temperature is normal, mild topology suitable for scenarios where the battery health is poor, and heat dissipation optimized topology suitable for scenarios where the battery temperature is too high. Each topology corresponds to a unique switching device conduction logic and module connection method.

[0011] Furthermore, the topology dynamic reconstruction module has a built-in topology switching judgment unit. After receiving the topology structure requirement signal output by the multi-objective optimization calculation module, the unit compares the difference between the current topology and the required topology. When the difference exceeds a preset threshold, the switching device control logic is activated to adjust the topology connection mode.

[0012] Furthermore, the liquid cooling coordinated control module adjusts the operating parameters of the liquid cooling heat dissipation circuit, including the coolant circulation flow rate and coolant temperature. When the received topology state is a heat dissipation optimized topology, the module calculates the coolant circulation flow rate adjustment value using a formula. The formula is: ,in, For flow adjustment coefficient, This refers to the actual temperature of the battery. This refers to the battery's safe temperature threshold. This represents the heat dissipation area of ​​the power devices after topology switching. Experiments were conducted to determine the correlation between coolant flow rate and heat dissipation effect under standard heat dissipation conditions, testing different temperature differences and heat dissipation areas. The flow rate adjustment parameters that stabilized the battery temperature to a safe threshold were recorded, and specific values ​​were determined through nonlinear fitting. The power device layout parameters are directly obtained from the topology dynamic reconstruction module based on the heat dissipation optimization topology.

[0013] Furthermore, the battery status sensing module has a built-in data verification unit that verifies the collected battery parameters in real time. When the parameter data error exceeds the preset range, a secondary acquisition process is triggered. If the secondary acquisition data is still abnormal, a fault warning signal is sent to the system control terminal.

[0014] Furthermore, the multi-objective optimization calculation module has a built-in charging time correction unit. When the user-preset charging time is too short, making it impossible to simultaneously meet the optimization objectives of battery life and system cost, this unit generates a time correction suggestion, which is displayed through the user interface. After the user confirms, the constraints are updated.

[0015] Furthermore, a data synchronization unit is provided between the topology dynamic reconstruction module and the liquid cooling collaborative control module. This unit uses a real-time communication protocol to transmit data and control the time difference between topology status feedback and heat dissipation parameter adjustment within a preset range.

[0016] Compared with existing technologies, the dynamic reconfiguration topology system of this liquid-cooled charging module has the following advantages: This invention uses a battery status sensing module to collect key parameters such as battery power, health status, and temperature in real time, solving the problems of incomplete and inaccurate battery status monitoring in existing technologies. This enables more precise topology adjustment. A multi-objective optimization calculation module constructs a multi-objective optimization model that includes charging efficiency, charging time, battery life, and system cost, addressing the problem that existing technologies only focus on a single objective and cannot balance multiple needs. This achieves coordinated optimization of multiple objectives during the charging process. A topology dynamic reconstruction module dynamically adjusts the topology structure according to the battery status, and a liquid-cooled collaborative control module optimizes heat dissipation, solving the problems that existing fixed topologies cannot adapt to different battery states and pose safety hazards. Ultimately, this invention achieves improved charging efficiency, reduced energy loss, extended battery life, and reduced system cost. While meeting users' charging time requirements, it also considers charging safety, efficiency, and economy, improving the applicability and practicality of liquid-cooled charging modules.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A schematic diagram of a dynamic reconfiguration topology system for a liquid-cooled charging module; Figure 2 A flowchart illustrating the dynamic reconfiguration topology system of a liquid-cooled charging module; Figure 3 This is a flowchart of the topology dynamic reconfiguration module. Detailed Implementation

[0020] 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.

[0021] This invention provides a dynamic reconfiguration topology system for a liquid-cooled charging module, see [link to relevant documentation]. Figure 1 The core of this system consists of a battery state sensing module, a multi-objective optimization calculation module, a topology dynamic reconstruction module, and a liquid-cooled collaborative control module. The functions and composition details of each module are as follows: The battery status sensing module is the core unit of the system for acquiring real-time battery status, integrating three core detection components: voltage detection, current detection, and temperature detection. The voltage detection component uses a high-precision voltage sensor to accurately acquire the battery terminal voltage; the current detection component uses a Hall effect current sensor to collect current data during battery charging and discharging; the temperature detection component includes a contact temperature sensor and a non-contact infrared temperature sensor. The former is attached to the battery casing to collect surface temperature, while the latter uses infrared detection technology to obtain the internal temperature of the battery. Simultaneously, the module has a built-in data verification unit that can perform real-time validity verification of the acquired parameters such as charge level, state of health (SOH), and temperature. If the parameter error exceeds the preset range, a secondary acquisition process is triggered; if the secondary acquisition is still abnormal, a fault warning signal is issued. Furthermore, the module also has a signal conversion function, which can convert the acquired analog signals into digital signals for transmission to subsequent modules.

[0022] The multi-objective optimization calculation module, based on the digital signals transmitted by the battery state sensing module, undertakes the tasks of multi-objective optimization analysis and parameter output. The module incorporates a weighted summation optimization algorithm, as well as a weight adjustment unit and a charging time correction unit. The weight adjustment unit can accept manually input weight coefficients through the user interface, or automatically generate weight coefficients for four optimization objectives—charging efficiency, charging time, battery life, and system cost—based on scenario characteristics, depending on the actual application scenario (such as fast charging for electric vehicles or charging for energy storage systems). The charging time correction unit generates time correction suggestions and displays them through the interface when the user-preset charging time is too short to simultaneously meet the optimization objectives of battery life and system cost. The constraints are updated after user confirmation. Finally, the module calculates and outputs the optimal charging parameters and topology requirement signals through the multi-objective optimization model.

[0023] The topology dynamic reconfiguration module is key to achieving dynamic topology adjustment. It pre-stores three topology schemes adapted to different battery states: a high-efficiency topology for scenarios with good battery health and normal temperature, a mild topology for scenarios with poor battery health, and a heat-optimized topology for scenarios with excessively high battery temperature. Each topology corresponds to a unique switching device conduction logic and module connection method. The module incorporates a topology switching judgment unit and a data synchronization unit. After receiving the topology requirement signal, the topology switching judgment unit compares the current topology with the required topology. If the difference exceeds a preset threshold, the switching device control logic is activated to adjust the topology connection method. The data synchronization unit uses a real-time communication protocol to transmit data with the liquid cooling co-control module, controlling the time difference between topology status feedback and heat dissipation parameter adjustment within a preset range to ensure efficient co-response.

[0024] The liquid cooling coordinated control module is responsible for the coordinated optimization of topology reconstruction and liquid cooling heat dissipation. It primarily adjusts the operating parameters of the liquid cooling circuit based on the topology status feedback from the topology dynamic reconstruction module, including coolant circulation flow rate and coolant temperature. The module receives topology status information (such as the current topology type and power device heat dissipation area) from the topology dynamic reconstruction module, combines it with the actual battery temperature, and generates liquid cooling parameter adjustment signals to control the operating status of components such as the circulation pump speed and temperature regulation device in the liquid cooling circuit. Simultaneously, the module has a heat dissipation status feedback function, collecting data such as coolant inlet and outlet temperatures and flow rates to generate heat dissipation status feedback signals, ensuring that the heat dissipation effect meets the requirements of the current topology and battery status. Example 1

[0025] This embodiment addresses the high-frequency and diverse charging needs of electric vehicle fast charging stations by applying a dynamic reconfiguration topology system of a liquid-cooled charging module to a 120kW high-power fast charging pile. It is compatible with electric vehicle batteries of different brands and service lives (with varying health conditions). See [link to documentation]. Figure 2and Figure 3 When an electric vehicle enters a fast charging station and connects to the charging gun, the system needs to monitor the battery status in real time. Combined with the charging time set by the user (such as a 30-minute fast charging requirement), the system dynamically adjusts the topology and liquid cooling parameters. This not only meets the user's need for rapid energy replenishment but also avoids excessive battery wear, reduces system operating costs, and solves the problems of poor adaptability and high charging safety risks of traditional fixed topology fast charging piles.

[0026] When a user initiates the charging process and inputs a preset charging time of 30 minutes through the interactive interface, the system first activates the battery status sensing module. Within this module, the voltage detection component (high-precision voltage sensor) collects the battery terminal voltage in real time, the current detection component (Hall current sensor) synchronously records the charging and discharging current during the initial charging phase, the contact temperature sensor adheres to the battery casing to collect surface temperature, and the non-contact infrared temperature sensor penetrates the battery casing to detect the internal temperature, ensuring comprehensive temperature distribution data of the battery is obtained.

[0027] Meanwhile, the module calculates the battery's State of Health (SOH) using a built-in algorithm, specifically employing the formula... (in , These are weighting coefficients determined through standard charge-discharge tests of the same battery model. This represents the current actual capacity of the battery. For the battery's rated capacity, The current internal resistance of the battery. (Based on the initial internal resistance of the battery), the parameters such as charge, SOH, surface temperature, and internal temperature are ultimately converted from analog signals to digital signals and transmitted to the multi-objective optimization calculation module. During this process, the module's built-in data verification unit verifies the collected parameters in real time. If the error of a certain parameter (such as internal temperature) exceeds the preset range, a secondary acquisition process is triggered. If the secondary acquisition data is still abnormal, a fault warning signal is immediately sent to the system control terminal to avoid making subsequent decisions based on erroneous data.

[0028] After receiving the digital signal transmitted by the battery state sensing module, the multi-objective optimization calculation module initiates the multi-objective optimization model construction process with a "30-minute preset charging time" as the constraint. The module uses a weighted summation method as the optimization algorithm. Its built-in weight adjustment unit, combined with the characteristics of fast charging scenarios for electric vehicles (users prioritize charging efficiency and time, followed by battery life and system cost), automatically generates weight coefficients for each optimization objective (satisfying...). ,in This is a weighting coefficient for charging efficiency. This is a weighting factor for charging time. This is a weighting factor for battery life. (This refers to the system cost weighting coefficient).

[0029] Then through the formula (in This represents the actual charging efficiency. The preset charging time is 30 minutes. The actual charging time is calculated in real time. This represents the theoretical maximum cycle life of the battery. This refers to the actual cycle life of the battery. The theoretical maximum cost threshold of the system. Calculate the multi-objective optimization comprehensive value (for the actual system cost). and based on The optimal solution outputs "optimal charging parameters" (such as charging voltage and current curves) and "topology requirement signals" (such as determining whether to enable a mild topology or a heat dissipation optimized topology based on battery SOH and temperature).

[0030] If the calculation process finds that the preset charging time of 30 minutes is too short, making it impossible to simultaneously meet the battery life and system cost optimization goals (e.g., forced fast charging will cause battery cycle loss to exceed the threshold), the module's built-in charging time correction unit will generate a time correction suggestion (e.g., suggesting an extension to 40 minutes) and display it to the user through the interactive interface. After the user confirms, the constraints will be updated and the optimization calculation will be performed again.

[0031] After receiving the "topology demand signal" output by the multi-objective optimization calculation module, the topology dynamic reconfiguration module first compares the current topology with the required topology using its built-in topology switching judgment unit. If the current topology is an "efficient topology" while the required topology is a "mild topology" (e.g., the battery SOH is below a preset threshold, indicating poor health), and the difference between the two exceeds the preset threshold, the module calls the pre-stored "mild topology" scheme. By controlling the on / off logic of switching devices such as IGBTs within the topology, it adjusts the connection method between modules (e.g., changing from series connection to mixed connection to reduce charging power per unit time) to complete the topology reconfiguration.

[0032] During the reconfiguration process, the module collects "current topology status information" (such as the operating status of switching devices and topology connection mode) in real time, and transmits the "topology operating status feedback signal" to the liquid cooling collaborative control module through a built-in data synchronization unit using a real-time communication protocol (such as the CAN bus protocol). Simultaneously, it receives the "heat dissipation status feedback signal" from the liquid cooling collaborative control module, keeping the time difference between the two within a preset range (e.g., within 50ms) to ensure the synergy between topology adjustment and heat dissipation optimization. If the difference between the current topology and the required topology does not exceed a threshold, the current topology is maintained, and only the "current topology status information" is fed back to the liquid cooling collaborative control module.

[0033] The liquid-cooled collaborative control module receives the "topology operation status feedback signal" (including the current topology type and the heat dissipation area of ​​the power devices) transmitted by the topology dynamic reconstruction module. After obtaining information such as the battery status sensing module, the actual battery temperature is then combined with this information. "Initiate the liquid cooling parameter adjustment process." If the current topology is a "heat dissipation optimization topology" (e.g., the battery internal temperature is higher than the safe temperature threshold), then... The module uses formulas (in Calculate the coolant circulation flow adjustment value using the flow adjustment coefficient determined through standard heat dissipation environment experiments. It also generates a "liquid cooling parameter adjustment signal": on the one hand, it controls the speed of the circulating pump in the liquid cooling circuit to increase the coolant flow rate. This accelerates heat removal efficiency; on the other hand, it controls the cooling device (such as a plate heat exchanger) to lower the coolant temperature and ensure that the coolant inlet temperature is maintained within a preset range. If the current topology is a "high-efficiency topology" or a "mild topology", the coolant circulation flow rate and temperature are adjusted to the appropriate range according to the heating characteristics of the power devices corresponding to the topology.

[0034] Meanwhile, the module collects the real-time flow rate and inlet / outlet temperature of the coolant through flow and temperature sensors, generates a "heat dissipation status feedback signal" and transmits it to the topology dynamic reconfiguration module to form a closed-loop control, ensuring that the battery temperature is always within a safe range during charging.

[0035] During the charging process, the system continuously repeats the above-mentioned "state perception - optimization calculation - topology adjustment - heat dissipation control" process until the battery reaches full charge or the user-preset 30-minute charging time is reached. At this time, the system automatically stops charging, disconnects the charging circuit from the liquid cooling circuit, and enters standby mode to wait for the next charging command.

[0036] In summary, this embodiment achieves dynamic adaptation in fast charging scenarios for electric vehicles through a complete "sensing-computation-reconstruction-regulation" process: On the one hand, the accurate collection and verification of multiple parameters by the battery status sensing module provides a reliable basis for topology adjustment, avoiding decision-making biases caused by single monitoring parameters in traditional systems; on the other hand, the multi-objective optimization calculation module balances charging efficiency, time, battery life, and system cost, solving the problem of traditional fast charging being "fast but not protective"; at the same time, the linkage between dynamic topology reconstruction and liquid cooling synergistic regulation ensures that batteries in different health states and at different temperatures can obtain suitable charging solutions, ultimately improving charging efficiency. Example 2

[0037] This embodiment addresses the long-term, high-stability charging requirements of large-scale energy storage systems (such as photovoltaic-supported energy storage power stations and grid peak-shaving energy storage stations). It applies a dynamically reconfigurable topology system of liquid-cooled charging modules to a 500kWh energy storage battery pack charging scenario. In this scenario, the energy storage battery pack needs to be charged during off-peak grid periods (such as at night), balancing charging efficiency, long-term battery cycle life, and system operation and maintenance costs. It also needs to address issues such as state of health (SOH) differences and localized temperature imbalances caused by long-term charging and discharging. Traditional fixed-topology charging modules are ill-suited to these complex requirements. (See [link to relevant documentation]). Figure 2 and Figure 3 This system can achieve multi-objective collaborative optimization through dynamic adjustment.

[0038] When the power grid dispatch system issues an energy storage charging command and sets the charging duration to 8 hours (the duration of the off-peak period), the system activates the battery status sensing module. This module, designed for the multi-cell series connection characteristics of energy storage battery packs, deploys high-precision voltage sensors (voltage detection components) at the positive and negative terminals of each battery cluster to collect the terminal voltage of each cluster in real time, preventing single-cell voltage anomalies from affecting the overall judgment. Hall effect current sensors (current detection components) are installed at the busbars of the energy storage battery pack to record the overall charging and discharging current, ensuring the accuracy of the current data. For temperature detection, in addition to uniformly distributing contact temperature sensors on the surface of the battery pack to collect surface temperature, non-contact infrared temperature sensors are also deployed in key heat-generating areas inside the battery pack (such as cell connections and near power devices) to accurately capture the internal temperature distribution.

[0039] At the same time, the module is based on the formula Current actual capacity of the pool cluster For the rated capacity of the battery cluster, The current internal resistance of the battery cluster, The system converts the initial internal resistance of the battery clusters into digital signals, and converts parameters such as charge, state of equilibrium (SOH) of each battery cluster, surface temperature, and internal temperature from analog signals. During this process, the data verification unit performs cross-verification on the collected parameters (such as comparing the temperature difference between adjacent battery clusters to determine if there are any anomalies). If the parameter error exceeds the preset range, a second acquisition is triggered. If the second acquisition is still abnormal, a fault warning is sent to the system control terminal, and the charging circuit of the corresponding battery cluster is cut off to ensure the safety of the overall system.

[0040] After receiving the digital signal from the battery state sensing module, the multi-objective optimization calculation module initiates the construction of a multi-objective optimization model with "8 hours of charging time" as the core constraint. Considering the high priority requirements of large-scale energy storage systems for battery life and system cost (energy storage batteries need to be used for long-term cycles, and maintenance costs account for a high proportion), the module's built-in weight adjustment unit automatically generates optimization target weight coefficients (satisfying the requirements of the battery state sensing module). ,in (Battery lifespan weighting factor) and (System cost weighting coefficient) has a higher proportion. (Charging efficiency weighting coefficient) and (Charging time weighting coefficient) is secondary. If maintenance personnel need to adjust the weighting (e.g., if the power grid requires a shorter charging time, the weighting needs to be increased), the weighting should be adjusted accordingly. The percentage can also be entered manually through the backend management interface.

[0041] Subsequently, the module uses the formula Calculate the comprehensive value of multi-objective optimization (in This represents the actual current charging efficiency. The preset charging time is 8 hours. For the actual charging time calculated in real time, This represents the theoretical maximum cycle life of the energy storage battery. This refers to the actual cycle life of the battery. The theoretical maximum cost threshold of the system. (Based on the actual operation and maintenance and energy consumption costs of the system), and based on The optimal solution outputs "optimal charging parameters" (such as a stepped charging current curve to avoid long-term high-current charging damage to the battery) and "topology requirement signals" (such as determining whether to enable a mild topology or a heat dissipation optimized topology based on the overall SOH and temperature distribution of the battery pack).

[0042] If the calculation finds that the 8-hour charging time is too short (e.g., the current capacity of the battery pack is too low, and a longer charging time is needed to avoid overcurrent), the charging time correction unit will generate a correction suggestion (e.g., suggesting an extension to 9 hours) and feed it back to the grid dispatch system. After confirmation, the constraints will be updated and the optimization parameters will be recalculated.

[0043] After receiving the "topology structure demand signal," the topology dynamic reconfiguration module first retrieves the pre-stored topology scheme library (including high-efficiency topology, mild topology, and heat-optimized topology) and compares the differences between the current topology and the required topology (e.g., the current topology is high-efficiency, and the requirement is mild topology). If the difference exceeds a preset threshold (e.g., the charging current limit corresponding to the topology, or the difference in heat dissipation design exceeds the safe range), the module activates the switching device control logic. By adjusting the on and off timing of switching devices such as IGBTs and relays, the module changes the power conversion topology connection method of the energy storage charging module (e.g., changing the series structure of the high-efficiency topology to the hybrid structure of the mild topology, reducing the charging power of a single module). If the difference does not exceed the threshold, the current topology is maintained, and only the charging parameters are updated.

[0044] After the topology adjustment is completed, the module transmits the "topology operation status feedback signal" (including the current topology type, power device layout, and heat dissipation area) through the data synchronization unit (using the industrial Ethernet real-time communication protocol). Information such as heat dissipation status is transmitted to the liquid cooling collaborative control module, while receiving the "heat dissipation status feedback signal" from the liquid cooling module to ensure that the time difference between the two data interactions is controlled within a preset range, thus avoiding local overheating caused by asynchronous topology adjustment and heat dissipation optimization.

[0045] The liquid cooling coordinated control module uses the "topology operation status feedback signal" from the topology dynamic reconstruction module, combined with the "actual battery temperature" transmitted by the battery status sensing module, to determine the optimal operating conditions. "Initiate liquid cooling parameter adjustment. If the current topology is "heat dissipation optimized topology" (e.g., the internal temperature of the battery pack is higher than the safe temperature threshold)..." The module uses formulas Calculate the coolant circulation flow rate adjustment value (in The flow adjustment coefficient is determined through experiments on the liquid cooling circuit of the energy storage system. (This refers to the heat dissipation area of ​​the power devices in the current topology) and generates a "liquid cooling parameter adjustment signal": controlling the speed of the liquid cooling circulation pump and increasing the coolant flow rate. At the same time, adjust the cooling power of the cooling water tank to reduce the coolant temperature; if the current topology is a "mild topology" or "high-efficiency topology", then adjust the coolant flow rate and temperature to the appropriate range according to the heat generation power corresponding to the topology (for example, if the heat generation power of the mild topology is low, the coolant flow rate can be reduced to reduce energy consumption).

[0046] In addition, the module collects heat dissipation data in real time through temperature sensors and flow sensors installed at the inlet and outlet of the liquid cooling circuit, generates a "heat dissipation status feedback signal" and transmits it to the topology dynamic reconfiguration module to form a closed-loop control, ensuring that the temperature of the energy storage battery pack remains stable within a safe range throughout the entire charging process.

[0047] During the charging process, the system repeats the "state perception-optimization calculation-topology adjustment-heat dissipation control" process every 30 minutes, dynamically adapting to changes in battery state (such as battery temperature rise during charging and dynamic updates of SOH) until the battery pack reaches full charge or completes 8 hours of charging. At this point, the system automatically stops charging, shuts down the liquid cooling circulation pump and cooling device, enters a low-power standby state, and simultaneously sends charging completion information back to the power grid dispatch system.

[0048] In summary, this embodiment fully leverages the system's dynamic adjustment advantages by adapting to the charging requirements of large-scale energy storage systems: the multi-region, multi-parameter acquisition and verification of the battery status sensing module solves the problem of incomplete state monitoring of energy storage battery packs; the dynamic weight allocation of the multi-objective optimization calculation module balances the requirements of lifespan, cost, and efficiency in energy storage scenarios; and the linkage between topology dynamic reconstruction and liquid cooling coordinated control avoids battery damage and localized overheating caused by long-term charging.

[0049] 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 simple 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. A dynamic reconfiguration topology system for a liquid-cooled charging module, characterized in that, The system includes: a battery state sensing module, a multi-objective optimization calculation module, a topology dynamic reconstruction module, and a liquid cooling collaborative control module; The battery status sensing module integrates a voltage detection component, a current detection component, and a temperature detection component. It is used to collect the battery's power, health status, battery surface temperature, and battery internal temperature parameters in real time, and convert the collected analog signals into digital signals and transmit them to the multi-objective optimization calculation module. The multi-objective optimization calculation module is based on the parameters transmitted by the battery state sensing module and takes meeting the user's preset charging time requirements as a constraint. It constructs a multi-objective optimization model that covers maximizing charging efficiency, ensuring that the charging time does not exceed the set range, minimizing battery cycle loss, and minimizing the sum of module energy consumption and maintenance costs. After processing by the optimization algorithm, it outputs the optimal charging parameters and topology structure requirement signal to the topology dynamic reconstruction module. The topology dynamic reconstruction module pre-stores topology schemes adapted to different battery states. After receiving the topology requirement signal, it adjusts the topology connection mode by controlling the on and off logic of the internal switching devices of the topology, completes the topology reconstruction, and synchronously feeds back the topology state to the liquid cooling collaborative control module. The liquid cooling coordinated control module adjusts the operating parameters of the liquid cooling heat dissipation circuit according to the topology state, so as to realize the coordinated optimization of topology reconstruction and liquid cooling heat dissipation.

2. The dynamic reconfiguration topology system for a liquid-cooled charging module according to claim 1, characterized in that, The battery state sensing module employs a high-precision voltage sensor for voltage detection, a Hall effect current sensor for current detection, and a contact temperature sensor and a non-contact infrared temperature sensor for temperature detection. The voltage detection component acquires the battery's terminal voltage, the current detection component acquires the battery's charging and discharging current, the contact temperature sensor acquires the battery's surface temperature, and the non-contact infrared temperature sensor acquires the battery's internal temperature. The battery state sensing module calculates the battery's state of health (SOH) using the following formula: In the formula, This represents the battery's current actual capacity. For the battery's rated capacity, The current internal resistance of the battery. The initial internal resistance of the battery, coefficient and These are the weighting coefficients, and .

3. The dynamic reconfiguration topology system for a liquid-cooled charging module according to claim 1, characterized in that, The optimization algorithm in the multi-objective optimization calculation module is a weighted summation method. This module has a built-in weight adjustment unit. This unit can receive manually input weight coefficients for each optimization objective through a user interface, or automatically generate weight coefficients for each optimization objective based on the characteristics of the charging scenario. The multi-objective optimization calculation module calculates the comprehensive value of the multi-objective optimization using a formula. The formula is: ,in, This is a weighting coefficient for charging efficiency. This is a weighting factor for charging time. This is a weighting factor for battery life. This is the system cost weighting coefficient, and This represents the actual charging efficiency. Preset charging time for users. Actual charging time This represents the theoretical maximum cycle life of the battery. This refers to the actual cycle life of the battery. The theoretical maximum cost threshold of the system. This represents the actual cost of the system.

4. The dynamic reconfiguration topology system for a liquid-cooled charging module according to claim 1, characterized in that, The topology dynamic reconstruction module pre-stores three types of topology schemes: a high-efficiency topology suitable for scenarios where the battery is in good health and the temperature is normal, a mild topology suitable for scenarios where the battery is in poor health, and a heat dissipation optimized topology suitable for scenarios where the battery temperature is too high. Each topology corresponds to a unique switching device conduction logic and module connection method.

5. The dynamic reconfiguration topology system for a liquid-cooled charging module according to claim 1, characterized in that, The topology dynamic reconstruction module has a built-in topology switching judgment unit. After receiving the topology structure requirement signal output by the multi-objective optimization calculation module, the unit compares the difference between the current topology and the required topology. When the difference exceeds a preset threshold, the switching device control logic is activated to adjust the topology connection mode.

6. The dynamic reconfiguration topology system for a liquid-cooled charging module according to claim 1, characterized in that, The liquid cooling co-control module adjusts the operating parameters of the liquid cooling heat dissipation circuit, including the coolant circulation flow rate and coolant temperature. When the received topology state is a heat dissipation optimized topology, the module calculates the coolant circulation flow rate adjustment value using a formula. The formula is: ,in, For flow adjustment coefficient, This refers to the actual temperature of the battery. This refers to the battery's safe temperature threshold. This represents the heat dissipation area of ​​the power device after topology switching.

7. The dynamic reconfiguration topology system for a liquid-cooled charging module according to claim 1, characterized in that, The battery status sensing module has a built-in data verification unit that verifies the collected battery parameters in real time. When the parameter data error exceeds the preset range, a secondary acquisition process is triggered. If the secondary acquisition data is still abnormal, a fault warning signal is sent to the system control terminal.

8. The dynamic reconfiguration topology system for a liquid-cooled charging module according to claim 1, characterized in that, The multi-objective optimization calculation module has a built-in charging time correction unit. When the user-preset charging time is too short and cannot simultaneously meet the optimization objectives of battery life and system cost, this unit generates a time correction suggestion, which is displayed through the user interface. After the user confirms, the constraints are updated.

9. The dynamic reconfiguration topology system for a liquid-cooled charging module according to claim 1, characterized in that, A data synchronization unit is provided between the topology dynamic reconstruction module and the liquid cooling collaborative control module. This unit uses a real-time communication protocol to transmit data and control the time difference between topology status feedback and heat dissipation parameter adjustment within a preset range.

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