Modular battery control method and system based on LLC resonant converter

Abstract

The application relates to the technical field of power electronics and battery management, and discloses a modular battery control method and system based on an LLC resonant converter, which comprises the following steps: acquiring real-time state parameters such as voltage, current and temperature of each battery module; constructing a multi-element differential relation parameter by calculating a spatial gradient parameter reflecting the state difference distribution among the modules, a time differential parameter reflecting the state change trend and a cross-coupling parameter reflecting the mutual influence degree among different state parameters; performing multi-objective collaborative optimization decision, quantifying the negative influence cost when the targets conflict and dynamically determining the priority, searching for a global compromise solution in the state space, which makes the system as a whole evolve smoothly to the equilibrium state, as a collaborative power distribution instruction set, driving the LLC resonant converter to perform power management on each module, realizing the paradigm shift from independent control of single-state to intelligent collaborative regulation and control of the dynamic coupling relationship among the modules, and effectively solving the dynamic inconsistency problem.

Description

Technical Field

[0001] This invention relates to the technical field of power electronics and battery management, and discloses a modular battery control method and system based on LLC resonant converter. Background Technology

[0002] With the rapid development of new energy storage and electric vehicles, high-voltage battery systems composed of multiple battery modules connected in series or in a hybrid configuration have become mainstream. LLC resonant converters, due to their high efficiency and soft-switching characteristics, are widely used as isolated charging and discharging interfaces for these modules. However, existing control strategies still have many shortcomings. For example, they typically make decisions based only on the single instantaneous state of the battery module, which is a static feedback control and cannot sense or predict the dynamic change trend of the state, resulting in a response lag. LLC frequency conversion control is used to achieve voltage equalization between modules, but it only responds to the instantaneous voltage value and cannot predict the state of charge (SOC) or temperature trend, resulting in a slow equalization speed. Existing SOC equalization strategies do not consider the temperature rise problem caused by high current equalization. Therefore, existing methods are difficult to solve the dynamic inconsistency problem in multi-state variable coupled scenarios. Summary of the Invention

[0003] To address the aforementioned technical problems, the main objective of this invention is to provide a modular battery control method and system based on an LLC resonant converter. The modular battery control method based on an LLC resonant converter includes:

[0004] S1 acquires the real-time status parameters of each of the plurality of battery modules, the status parameters including voltage, current and temperature;

[0005] S2 calculates the multivariate differential relationship parameters of the interaction between the battery modules using the real-time state parameters of each battery module;

[0006] S3 uses the multivariate differential relationship parameters to perform multi-objective collaborative optimization decision-making and generate a collaborative power allocation instruction set corresponding to the multiple battery modules;

[0007] When there is a conflict between multiple control objectives, the negative impact cost of a single battery module on the associated objectives is calculated by cross-coupling parameters, and the real-time priority of each control objective is determined by spatial gradient parameters and time differential parameters.

[0008] S4 uses the negative impact cost and the real-time priority to find a global compromise solution in the state space that enables the overall evolution of the multivariate differential relation parameters to a stable equilibrium state, which serves as the cooperative power allocation instruction set.

[0009] As a preferred embodiment of the modular battery control method based on an LLC resonant converter according to the present invention, wherein:

[0010] The multivariate differential relation parameters include spatial gradient parameters reflecting the distribution of state differences between modules, temporal differential parameters reflecting the trend of state changes in each module, and cross-coupling parameters reflecting the degree of mutual influence between state parameters of different dimensions.

[0011] As a preferred embodiment of the modular battery control method based on an LLC resonant converter according to the present invention, wherein:

[0012] A multi-dimensional state space is constructed using the real-time state parameters of all battery modules;

[0013] The multidimensional state space obtains the spatial gradient parameter by calculating the ratio or statistical distribution characteristics of the changes in the same state parameter of any two different battery modules, which is used to quantify the spatial distribution pattern of state inconsistency.

[0014] By performing time-dependent differential operations on the various state parameters of each battery module, the time differential parameter is obtained, which is used to quantify the instantaneous change rate and trend of the state of each module.

[0015] The cross-coupling parameter is obtained by calculating the correlation ratio between the changes in at least two different dimensions of state parameters of any battery module, and is used to quantify the dynamic interaction strength between different physical quantities.

[0016] As a preferred embodiment of the modular battery control method based on an LLC resonant converter according to the present invention, wherein:

[0017] Methods for calculating the cost of negative impacts include:

[0018] S3011 takes the cross-coupling parameters, the expected control action for the target battery module, and the unit change in the first state parameter of the module caused by the control action as inputs;

[0019] S3012 The unit change amount is used to calculate the predicted change amount of the second state parameter of the target battery module through the mapping relationship of the cross-coupling parameters;

[0020] S3013 outputs the predicted change as the cost of the negative impact incurred when executing the control action to achieve the first objective.

[0021] Methods for determining real-time priority include:

[0022] S3021 determines the baseline priority of each control objective based on the state differences reflected by the spatial gradient parameters;

[0023] S3022 dynamically adjusts the benchmark priority based on the rate of state change and acceleration trend indicated by the time differential parameter.

[0024] As a preferred embodiment of the modular battery control method based on an LLC resonant converter according to the present invention, wherein:

[0025] The multivariate differential relation parameters are input into the optimization solver.

[0026] The spatial gradient parameter is used to construct the optimization objective term regarding state consistency, the time differential parameter is used to construct the constraint term regarding the dynamic response speed and stability of the system, and the cross-coupling parameter is used to construct the coupling cost term describing the mutual constraints between different state variables.

[0027] The optimization solver constructs a comprehensive cost function with optimization objective terms and coupling cost terms, and uses constraint terms as boundary conditions. By minimizing this comprehensive cost function, it solves for the optimal prediction vector of the power demand of each battery module within a future finite time window.

[0028] As a preferred embodiment of the modular battery control method based on an LLC resonant converter according to the present invention, wherein:

[0029] The optimal prediction vector is decoupled and decomposed into a set of instantaneous power setting values ​​that correspond one-to-one with each battery module, are synchronized in time and coordinated in numerical value, based on the real-time state of each battery module and their interrelationship in the multivariate differential relation parameters, thus forming the cooperative power allocation instruction set.

[0030] The instruction set is used to ensure that electrical connection and thermal constraints are met, and the power setpoint is used to implement the coordinated operation of optimal prediction vector planning and drive the synchronous response of each module.

[0031] As a preferred embodiment of the modular battery control method based on an LLC resonant converter according to the present invention, wherein:

[0032] Construct a Lyapunov function with the aforementioned multivariate differential relation parameters as key variables;

[0033] The spatial gradient parameter defines the potential energy surface of the Lyapunov function, the cross-coupling parameter defines the curvature of the potential energy surface, and the time differential parameter is used to evaluate the instantaneous kinetic energy of the system's state trajectory.

[0034] The global compromise solution is the state evolution direction selected in the energy field described by the Lyapunov function, which causes the sum of the system's potential energy and kinetic energy to decay the fastest and has the smoothest evolution path. The control input corresponding to this direction is the global compromise solution.

[0035] As a preferred embodiment of the modular battery control method based on an LLC resonant converter according to the present invention, wherein:

[0036] The Lyapunov function embeds the quantized cost into a nonlinear constraint in the state space, and transforms the dynamic priority into differentiated requirements for the convergence speed of different segments of the state trajectory.

[0037] The global compromise solution is obtained by solving the constrained gradient flow optimization problem. The global compromise solution is used to enable the system state to adaptively bypass the obstacles formed by high-cost regions when moving along the negative gradient direction of the energy field, and to prioritize the convergence speed requirements corresponding to high-priority objectives.

[0038] As a preferred embodiment of the modular battery control method based on an LLC resonant converter according to the present invention, wherein:

[0039] The global compromise solution is inversely solved using a pre-set inverse system mapping model to generate the power injection vector required to produce the current evolution direction under the current system constraints.

[0040] The power injection vector is the cooperative power allocation instruction set, where each component corresponds to the instantaneous power setting value of the battery module.

[0041] As a preferred embodiment of the modular battery control system using an LLC resonant converter according to the present invention, wherein:

[0042] The module consists of a state awareness module, a collaborative decision-making module, and a control module.

[0043] The state perception module includes a signal acquisition unit and a multivariate differential calculation unit. The signal acquisition unit is used to acquire the real-time state parameters of voltage, current and temperature of each battery module. The multivariate differential calculation unit is connected to the signal acquisition unit and is used to calculate, based on the real-time state parameters, a spatial gradient parameter reflecting the distribution of state differences between modules, a temporal differential parameter reflecting the trend of state changes of each module, and a cross-coupling parameter reflecting the degree of mutual influence between state parameters of different dimensions.

[0044] The collaborative decision-making module is used to make multi-objective collaborative optimization decisions based on the multivariate differential relationship parameters. When there is a conflict between multiple control objectives, the module calculates the negative impact cost of a single control action on the associated objectives through the cross-coupling parameters, and dynamically determines the real-time priority of each control objective through the spatial gradient parameters and time differential parameters, and finds a global compromise solution in the state space that enables the multivariate differential relationship parameters to evolve smoothly towards an equilibrium state.

[0045] The control module is used to demap the global compromise and generate a set of real-time coordinated control parameters for the LLC resonant converter, so as to drive the converter to perform differentiated and coordinated power management operations on different battery modules simultaneously.

[0046] The beneficial effects of this invention are:

[0047] This application introduces and calculates time differential parameters and cross-coupling parameters, which can not only sense the current state of the battery module, but also predict the state evolution trend and the internal interaction between the parameters. This makes the control decision forward-looking, can intervene in potential risks in advance, and puts multiple objectives such as balance, efficiency, and thermal management under a unified framework for collaborative optimization, avoiding secondary problems caused by single-objective optimization.

[0048] This application precisely locates inconsistent structural problems through spatial gradient parameters, and combines the search for global compromise solutions to implement the most effective differentiated power allocation. By using Lyapunov functions, it ensures that the system state evolves along the smoothest path of fastest energy decay, thereby reducing voltage and current stress and control oscillations during the equilibrium process while achieving rapid convergence.

[0049] This application uses multivariate differential relationship parameters as the core basis for system cognition and decision-making, realizing a control logic aimed at the global optimization of the system. The operating point of the LLC converter can be dynamically adjusted to the comprehensive optimal region, improving overall energy efficiency, realizing the management of coupling relationship, suppressing the development of inconsistency, and improving the overall reliability and long-term cycle life of the battery pack. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0051] Figure 1 This is a flowchart of a modular battery control method based on an LLC resonant converter according to the present invention;

[0052] Figure 2 This is a system composition diagram of a modular battery control system based on an LLC resonant converter according to the present invention;

[0053] Figure 3 This is a flowchart illustrating the mapping from global compromise solution to LLC execution in a modular battery control method based on an LLC resonant converter according to the present invention.

[0054] Figure 4This is a flowchart illustrating the calculation of multivariate differential parameters in a modular battery control method based on an LLC resonant converter, as described in this invention. Detailed Implementation

[0055] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0056] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0058] Example 1.

[0059] like Figure 1 As shown, a modular battery control method based on an LLC resonant converter includes:

[0060] S1 acquires the real-time status parameters of each of the plurality of battery modules, the status parameters including voltage, current and temperature;

[0061] Specifically, the real-time status parameters of each battery module in the battery system are obtained.

[0062] The state parameters are basic physical quantities that reflect the real-time operating status of the battery module, including at least: the output voltage of the battery module, the current flowing through the battery module, and the temperature of the battery module body.

[0063] Furthermore, the voltage, current, and temperature data are obtained directly through sensors deployed in each battery module.

[0064] S2 calculates the multivariate differential relationship parameters of the interaction between the battery modules using the real-time state parameters of each battery module;

[0065] Specifically, such as Figure 4 As shown, characteristic quantities of multivariate differential relation parameters are calculated and constructed from the real-time state parameters of the multiple battery modules.

[0066] The multivariate differential relation parameters include spatial gradient parameters, temporal differential parameters, and cross-coupling parameters.

[0067] The multivariate differential relation parameters are calculated to form the system relation characteristics from discrete state data.

[0068] The multivariate differential relation parameters include spatial gradient parameters reflecting the distribution of state differences between modules, temporal differential parameters reflecting the trend of state changes in each module, and cross-coupling parameters reflecting the degree of mutual influence between state parameters of different dimensions.

[0069] A preferred computational implementation method includes: constructing a multi-dimensional state space based on the real-time state parameters of all battery modules. In the multi-dimensional state space, each battery module corresponds to a coordinate point, and the dimension of the coordinate point is determined by the number of state parameters of the battery module.

[0070] For the calculation of spatial gradient parameters, a preferred implementation method includes: in the multidimensional state space, for the same state parameter, calculating the ratio of the changes in the state parameter values ​​of any two different battery modules, and obtaining the spatial gradient parameters through the calculation, wherein the spatial gradient parameters are used to quantify the distribution pattern of state inconsistencies among modules.

[0071] For the calculation of time differential parameters, a preferred implementation method includes: performing time difference operation on each state parameter of each battery module. The difference operation is completed by obtaining the state parameter values ​​at the current time and the previous time and calculating the ratio of their change to the time interval. The time differential parameter is obtained through the operation and is used to quantify the instantaneous change rate of the state of each module.

[0072] The time differential parameter is used to construct constraints on the system's response speed and stability.

[0073] Specifically, inequality constraints are constructed by limiting the maximum variation of any state parameter within a single control cycle.

[0074] The threshold for the maximum change range is set based on the battery's physical limits and safety specifications. For example, the temperature rise rate threshold ΔTmax / Δt is calculated using the battery cell's specific heat capacity, heat dissipation conditions, and thermal runaway initiation temperature. The voltage change rate threshold is determined based on the battery's polarization voltage characteristics and BMS sampling data.

[0075] For the calculation of cross-coupling parameters, a preferred implementation method includes: for any battery module, calculating the correlation ratio between the changes in at least two different dimensions of the battery module per unit time, and obtaining the cross-coupling parameters through the calculation. The cross-coupling parameters are used to quantify the coupling strength of different state parameters such as voltage, current, and temperature.

[0076] A multi-dimensional state space is constructed using the real-time state parameters of all battery modules;

[0077] The multidimensional state space obtains the spatial gradient parameter by calculating the ratio or statistical distribution characteristics of the changes in the same state parameter of any two different battery modules, which is used to quantify the spatial distribution pattern of state inconsistency.

[0078] By performing time-dependent differential operations on the various state parameters of each battery module, the time differential parameter is obtained, which is used to quantify the instantaneous change rate and trend of the state of each module.

[0079] The cross-coupling parameter is obtained by calculating the correlation ratio between the changes in at least two different dimensions of state parameters of any battery module, and is used to quantify the dynamic interaction strength between different physical quantities.

[0080] It should be noted that the calculation of the spatial gradient parameter is used to represent the inconsistent structural distribution; the calculation of the time differential parameter is used to represent the trend information of state evolution; and the calculation of the cross-coupling parameter is used to represent the mutual constraints of internal physical quantities. The calculation process is executed by the processing unit in the system.

[0081] In the calculation of the multivariate differential relation parameters, the weighting coefficients are used to weight different state parameters.

[0082] One implementation method for determining the weighting coefficients includes: during the system design phase, assigning initial weights to each state parameter, including voltage, SOC, and temperature, based on the battery cell specifications, thermal runaway critical conditions, and cycle life test data.

[0083] During system operation, historical data is recorded, and parameter estimation algorithms such as recursive least squares or gradient descent with forgetting factor are used to fine-tune the initial weights online to adapt them to the actual aging characteristics and operating environment of the battery pack.

[0084] S3 uses the multivariate differential relationship parameters to perform multi-objective collaborative optimization decision-making and generate a collaborative power allocation instruction set corresponding to the multiple battery modules;

[0085] When there is a conflict between multiple control objectives, the negative impact cost of a single battery module on the associated objectives is calculated by cross-coupling parameters, and the real-time priority of each control objective is dynamically determined by spatial gradient parameters and time differential parameters.

[0086] In this application, the terms in the multivariate differential relation parameters are defined as follows:

[0087] The first state parameter is the target state that is directly and actively adjusted through control actions, such as a planned increase in the state of charge or a planned decrease in the voltage.

[0088] The second state parameter is the change of the first state parameter, which is the affected state that is passively generated by the physical coupling relationship, such as the temperature rise due to the increase of charging current.

[0089] Specifically, a preferred implementation method for collaborative optimization decision-making includes:

[0090] The implementation methods for calculating the cost of negative impacts include:

[0091] S3011, the cross-coupling parameter, the expected control action for the target battery module, and the unit change in the first state parameter of the target battery module caused by the expected control action are taken as inputs.

[0092] S3012, the unit change is calculated using the mapping relationship defined by the cross-coupling parameters, and the predicted change of the second state parameter of the target battery module is output. The mapping relationship reflects the interaction strength between the first state parameter and the second state parameter.

[0093] S3013, the predicted change is output as a negative impact cost incurred when the expected control action is performed to achieve the first control objective, and the calculation process quantifies the potential side effects of the control action.

[0094] The implementation methods for determining real-time priority include:

[0095] S3021, Based on the state differences between modules reflected by the spatial gradient parameters, determine the initial baseline priority of each control objective. The more significant the state difference in a dimension, the higher the baseline priority obtained by its corresponding equilibrium control objective.

[0096] S3022, Combining the rate of state change and acceleration trend indicated by the time differential parameter, the reference priority is dynamically adjusted. For parameters whose rate of state change increases or shows a deteriorating and accelerating trend, the priority of the control target associated with the parameter is increased.

[0097] Using the negative impact cost and the real-time priority information, the collaborative optimization decision-making process searches for a global compromise solution in the system state space defined by the multivariate differential relation parameters through an optimization algorithm.

[0098] The global compromise solution can balance conflicting objectives and cause the overall dynamic relationship of the system described by the multivariate differential relation parameters to evolve towards an equilibrium state.

[0099] The global compromise solution generates a set of coordinated power allocation instructions corresponding to the multiple battery modules. The instruction set contains a set of instantaneous power settings that are synchronized in time and coordinated in value, with each setting corresponding to a battery module.

[0100] S4 uses the negative impact cost and the real-time priority to find a global compromise solution in the state space that enables the overall evolution of the multivariate differential relation parameters to a stable equilibrium state, which serves as the cooperative power allocation instruction set.

[0101] The multivariate differential relation parameters are input into the optimization solver.

[0102] The spatial gradient parameter is used to construct the optimization objective term regarding state consistency, the time differential parameter is used to construct the constraint term regarding the dynamic response speed and stability of the system, and the cross-coupling parameter is used to construct the coupling cost term describing the mutual constraints between different state variables.

[0103] A preferred implementation of constructing a state consistency optimization objective term includes: the optimization objective term is constructed using the spatial gradient parameter as the core variable.

[0104] Specifically, the optimization objective term for state consistency is composed of the weighted sum of squares of the spatial gradient parameters, which quantifies the state differences between modules. Minimizing the norm of the spatial gradient parameters directly drives the states of all battery modules to converge toward the average value, which is equivalent to minimizing the overall inconsistency of the system.

[0105] A preferred implementation of constructing system dynamic response speed and stability constraints includes: the constraints are constructed using the time differential parameter, which quantifies the rate of change of the state; the constraints are obtained by limiting the absolute value of the time differential parameter. For example, inequality constraints are constructed to limit the maximum change of any state parameter within a single control cycle, preventing abrupt state changes. The constraints ensure that the power command generated by the optimizer does not cause drastic fluctuations in battery voltage, current, or temperature, thus guaranteeing control stability and system safety.

[0106] The optimization solver constructs a comprehensive cost function with optimization objective terms and coupling cost terms, and uses constraint terms as boundary conditions. By minimizing this comprehensive cost function, it solves for the optimal prediction vector of the power demand of each battery module within a future finite time window.

[0107] A preferred implementation of constructing a coupling cost term includes: the coupling cost term is constructed based on the cross-coupling parameter, which defines the correlation ratio between changes in state variables of different dimensions.

[0108] The coupling cost term is reflected as a penalty term in the comprehensive cost function. This penalty term is composed of the cross-coupling parameter as a coefficient and the product of the change in the associated state variable.

[0109] When optimization attempts to change a state variable, if the cross-coupling parameter worsens another state variable, the coupling cost term will generate a high penalty value, guiding the optimization solver to avoid such high-cost operation paths.

[0110] A preferred implementation of the optimization solver includes: the optimization solver employs rolling optimization with model predictive control.

[0111] Specifically, in each control cycle, the optimization solver receives the multivariate differential relation parameters at the current moment as input. The optimization solver contains a battery model, which is used to predict the system dynamic model. The construction method combines the equivalent circuit model and thermal model of the battery module with the steady-state power transfer model of the LLC resonant converter and the system topology constraints.

[0112] Specifically, within each prediction step, based on the current state and the given power command, the output current of each port is first calculated through the converter model, then distributed to each battery module according to Kirchhoff's laws, and finally input to the equivalent circuit and thermal model of each battery to calculate the predicted state at the next moment.

[0113] A preferred method for constructing a battery model employs an equivalent circuit model and empirical formulas:

[0114] The battery's terminal voltage response is simulated by using a first-order or second-order RC equivalent circuit. This is achieved by applying a standard pulse discharge test to the battery and then identifying it online using the recursive least squares method.

[0115] The battery module is simplified as a homogeneous heat source. The initial values ​​of the battery module's heat capacity and thermal resistance parameters can be obtained by calculating the battery's geometric dimensions and material properties, and then corrected using temperature rise experimental data.

[0116] The cross-coupling parameters are used to quantify the coupling terms in the model. For example, temperature is correlated with internal resistance through the Arrhenius equation, and the SOC change is correlated with heat generation using the Coulomb efficiency factor. The coupling coefficients are obtained by conducting charge-discharge tests under varying temperature conditions and analyzing the data.

[0117] Furthermore, the solution process is as follows: the optimization solver adds the optimization objective term and the coupling cost term to form the comprehensive cost function.

[0118] The comprehensive cost function is used to represent the prediction and evaluation of system performance over a future period of time.

[0119] The constraint term constructed by the time differential parameter is applied to the optimization solver's prediction process of the future state trajectory. Under the constraint term, the optimization solver performs a minimization operation on the comprehensive cost function.

[0120] The minimization operation searches for the preset power command sequence of each battery module within a finite future time window. By solving the constrained optimization problem, the optimization solver outputs the optimal preset power command sequence.

[0121] Furthermore, the optimization solver extracts the power command corresponding to the first control moment from the optimal command sequence to form the optimal prediction vector for the power demand of each battery module within a future finite time window.

[0122] The optimal prediction vector is the set of cooperative power allocation instructions to be executed in the current week. In the next control cycle, the optimization solver updates the multivariate differential relation parameters with new measurements, repeats the optimization process, and realizes closed-loop feedback and control.

[0123] The optimal prediction vector is decoupled and decomposed into a set of instantaneous power setting values ​​that correspond one-to-one with each battery module, are synchronized in time and coordinated in numerical value, based on the real-time state of each battery module and their interrelationship in the multivariate differential relation parameters, thus forming the cooperative power allocation instruction set.

[0124] A preferred implementation includes: relying on a preset inverse system mapping model and a real-time feedback compensation mechanism.

[0125] The optimal prediction vector is a system-level power demand description. Decomposing the power demand into instantaneous power setpoints corresponding to each battery module requires an inverse system mapping model.

[0126] The inverse system mapping model encodes the power transfer characteristics of the LLC resonant converter, the electrical coupling relationship of each output port, and the equivalent impedance characteristics of the battery module. By inputting the optimal prediction vector and the real-time state of each battery module, such as the current terminal voltage, into the inverse model, the inverse model calculates the instantaneous power setpoint that needs to be injected or extracted from each battery module port to achieve the system-level power requirements.

[0127] Furthermore, to ensure that the instantaneous power setpoints are synchronized in time and coordinated in value, the decomposition process introduces the interrelated information in the multivariate differential relation parameters as a coordination factor.

[0128] Specifically, by utilizing the cross-coupling parameters, when calculating the power setting value of a certain module, the expected impact of this setting value on adjacent modules through electrical or thermal coupling is considered simultaneously, and pre-compensation is performed.

[0129] For example, if applying charging power to module A might slightly affect the temperature of module B through thermal coupling, this effect will be taken into account and a minor adjustment will be made when calculating the power setting value of module B. The spatial gradient parameter and the time differential parameter are used to dynamically adjust the weight of the coordination factor to ensure a balance between rapid equilibration and smooth operation.

[0130] By solving the inverse model and correcting the coordination factor, a set of instantaneous power setpoints that take effect in the same control cycle and are numerically complementary are output, forming the coordinated power allocation instruction set.

[0131] The instruction set is used to ensure that electrical connection and thermal constraints are met, and the power setpoint is used to implement the coordinated operation of optimal prediction vector planning and drive the synchronous response of each module.

[0132] A preferred instruction set for ensuring constraint satisfaction and driving cooperative operation includes the following specific implementation method:

[0133] The instruction set satisfies electrical and thermal constraints by prioritizing the constraints in the decoupling process.

[0134] An example of satisfying electrical connection constraints includes: the electrical connection relationships determined by the LLC converter topology and battery series-parallel structure are already built into the inverse system mapping model, such as Kirchhoff's laws constraints on the voltage and current between ports.

[0135] All instantaneous power setpoints obtained from the inverse model automatically satisfy the electrical connection constraints of the system and will not generate electrical conflicts or infeasible commands.

[0136] A specific implementation method to satisfy thermal constraints:

[0137] By using the time differential parameter as a stability constraint, the drastic temperature rise that might be caused by power commands is limited from the source.

[0138] The cross-coupling parameter is used for the coordination factor calculation. When the calculated power setpoint, combined with the real-time temperature and the cross-coupling parameter, predicts that the temperature will exceed the safety threshold, the decomposition process will automatically limit the setpoint proportionally and adjust the setpoints of other modules to maintain the total power balance of the system.

[0139] The technical logic behind using the power setting value to drive the coordinated operation of the system is as follows: the optimal prediction vector plans the overall system to move smoothly towards an equilibrium state in the near future, and the coordinated power allocation instruction set is used to issue commands to each battery module to execute the smooth movement to the equilibrium state.

[0140] When the LLC converter executes all instructions simultaneously, it drives the entire battery pack system to complete the coordinated state evolution within one control cycle along the planned optimal trajectory.

[0141] Construct a Lyapunov function with the aforementioned multivariate differential relation parameters as key variables;

[0142] The spatial gradient parameter defines the potential energy surface of the Lyapunov function, the cross-coupling parameter defines the curvature of the potential energy surface, and the time differential parameter is used to evaluate the instantaneous kinetic energy of the system's state trajectory.

[0143] The global compromise solution is the state evolution direction selected in the energy field described by the Lyapunov function, which causes the sum of the system's potential energy and kinetic energy to decay the fastest and has the smoothest evolution path. The control input corresponding to this direction is the global compromise solution.

[0144] The Lyapunov function is a scalar energy function, and the inputs are the multivariate differential relation parameters, namely the spatial gradient parameter vector, the time differential parameter vector, and the cross-coupling parameter matrix.

[0145] The construction of the function includes: a potential energy term, a kinetic energy term, and a coupling penalty term.

[0146] A preferred method for calculating the potential energy term includes: the potential energy term is calculated from the spatial gradient parameter.

[0147] Specifically, each component of the spatial gradient parameter vector is squared, then multiplied by a preset positive weighting coefficient, and finally all the weighted squared values ​​are summed.

[0148] The weighting coefficients are based on the corresponding state parameters. For example, when the spatial gradient parameters of voltage, SOC, and temperature are zero vectors, the potential energy term is zero. Furthermore, the greater the state difference, the higher the calculated potential energy term value.

[0149] Specifically, the kinetic energy term is calculated from the time differential parameter.

[0150] Squaring each component of the time differential parameter vector, multiplying each component by another preset positive weighting coefficient, and finally summing all the weighted squared values.

[0151] The weighting coefficients are used to adjust the degree of emphasis placed on the rate of change of different states.

[0152] The coupling penalty term is calculated using the cross-coupling parameter matrix and the planned change vector of the system state.

[0153] Specifically, the cross-coupling parameter matrix defines unit mutual influence coefficients between different state change quantities. The planned state change vector is multiplied by the cross-coupling parameter matrix, and then a dot product is performed with the transpose of the planned state change vector. The result of the dot product is a scalar, which quantifies the total cost incurred due to the internal coupling between state variables if the planned state change is executed. The greater the impact of the planned change on strongly coupled state variables, the higher the value of this term.

[0154] The final value of the Lyapunov function is obtained by summing the calculation results of the potential energy term, kinetic energy term, and coupling penalty term.

[0155] A preferred mapping relationship between potential energy surface, curvature, and kinetic energy.

[0156] The potential energy term is on the potential energy surface quantized by the state variables of all battery modules. The spatial gradient parameter is the direction and steepness of the potential energy surface at the current slope point of the battery module, and the gradient direction points to the direction in which the potential energy increases the fastest.

[0157] The curvature of the potential energy surface, defined as its bending characteristics in different directions, is determined by the cross-coupling parameter matrix in the coupling penalty term. The off-diagonal elements of this cross-coupling parameter matrix represent the coupling reaction force generated in the vertical direction when the state changes along a certain direction, leading to the distortion and ruggedness of the potential energy surface. A path with high curvature means that changing the state requires overcoming greater internal coupling resistance.

[0158] The kinetic energy term is calculated using the time differential parameter obtained through sensor data differential analysis.

[0159] The optimization problem with constraints is calculated using an iterative algorithm, and the output is the global compromise solution, which serves as the control input vector.

[0160] The optimization objective is to find a control input that, while satisfying the constraints, allows the Lyapunov function value to decrease most rapidly in the shortest possible time.

[0161] The constraints include: dynamic constraints, path smoothing constraints, and safety boundary constraints.

[0162] The control inputs in the dynamic constraints conform to the rated parameters of the battery module and LLC resonant converter.

[0163] In path smoothing constraints, the rate of change of the state, i.e. the rate of change of the time differential parameter itself, is limited to a preset change threshold to prevent control commands from causing state oscillations.

[0164] The state of each battery module at the next moment predicted by the safety boundary constraints is always within the preset safety upper and lower limits.

[0165] A preferred solution algorithm follows the iterative steps below:

[0166] A. Based on the current measured system state, calculate the multivariate differential relation parameters and Lyapunov function values.

[0167] B. Calculate the gradient of the rate of change of the Lyapunov function over time relative to the control input vector. The direction of the gradient is used to indicate the adjustment of the control input to reduce the total energy of the system.

[0168] C. Propose an adjustment direction for the control input along the negative gradient direction, and project the adjustment direction onto the feasible region defined by all the constraints. The projection operation is used to ensure that the adjusted direction not only reduces energy but also satisfies all safety and dynamic constraints.

[0169] D. Perform a one-dimensional search along the adjustment direction obtained by projection to find the optimal adjustment step size, such that the time derivative of the Lyapunov function is minimized after moving the step size along this direction.

[0170] E. Update the control input vector using the found step size. Once the convergence condition is met, the resulting control input vector is output as the global compromise solution for the current control cycle.

[0171] The Lyapunov function embeds the quantized cost into a nonlinear constraint in the state space, and transforms the dynamic priority into differentiated requirements for the convergence speed of different segments of the state trajectory.

[0172] In the optimization of the Lyapunov function, the negative impact cost and dynamic priority calculated in step S3 are systematically integrated into the solution process to ensure the global trade-off characteristics of the obtained solution.

[0173] A preferred embodiment of the fusion includes:

[0174] The negative impact cost is embedded as a nonlinear constraint. The negative impact cost, calculated through cross-coupling parameters, serves as a nonlinear inequality constraint in the state space. For example, the temperature rise caused by charging should not exceed ΔTmax, or the additional cycle aging cost caused by equalization operations should be below a certain threshold.

[0175] The real-time priority determined dynamically by spatial gradient and time differential parameters is transformed into differentiated expectations of the convergence speed of the system state trajectory in different dimensions.

[0176] In dynamic prioritization, larger weight coefficients are assigned to the state variables corresponding to high-priority objectives, resulting in a faster descent rate for those objectives. This allows the system state to meet the objectives more quickly as it moves along the overall negative gradient direction.

[0177] After obtaining the global compromise solution, the global compromise solution is converted into the specific instantaneous power setting value of each control channel of the LLC resonant converter.

[0178] A preferred embodiment includes:

[0179] The inverse system mapping model is a mathematical model used to characterize the inverse relationship between the desired power distribution effect of the system and the specific electrical parameter commands to be executed at each port of the LLC converter.

[0180] The mathematical model is obtained by integrating the steady-state and dynamic gain characteristics of the LLC resonant converter, the relationship between each output port, and the equivalent impedance characteristics of the battery module at the current operating point.

[0181] The global compromise solution is used as input to the inverse system mapping model. Based on the actual constraints of the current system, the inverse system mapping model calculates a set of instantaneous power setting values ​​corresponding to the evolution effect of the global compromise solution at the current moment. The power setting values ​​are the expected current command or expected power command for each battery module port.

[0182] A preferred method for establishing an inverse system mapping model includes:

[0183] During the development phase of the LLC resonant converter, an experimental platform was built. Within a given input voltage range, the switching frequency and the phase difference between each port were changed, and the steady-state voltage and current of each output port under each combination were measured and recorded.

[0184] Based on the measurement data, with switching frequency and phase difference as independent variables and voltage gain at each port as dependent variables, a database of the forward transmission characteristics of the LLC converter is established using polynomial surface fitting or piecewise linear interpolation methods.

[0185] The forward transmission characteristic database, through table lookup and iterative calculation, solves for a set of switching frequencies and phase difference parameters that can achieve the output for any set of desired port power output values, thus forming the inverse system mapping model.

[0186] The instantaneous power setting value, which corresponds one-to-one with each battery module and is numerically coordinated, is calculated through inverse kinematics, thus forming the final set of coordinated power allocation instructions.

[0187] The instruction set is directly sent to the digital controller of the LLC resonant converter to drive the generation of multiple PWM signals, thereby realizing the coordinated power management and state balancing operation of the entire battery module group.

[0188] The global compromise solution is obtained by solving the constrained gradient flow optimization problem. The global compromise solution is used to enable the system state to adaptively bypass the obstacles formed by high-cost regions when moving along the negative gradient direction of the energy field, and to prioritize the convergence speed requirements corresponding to high-priority objectives.

[0189] The global compromise solution is inversely solved by using a pre-set inverse system mapping model to obtain the power injection vector required to generate the current evolution direction under the current system constraints.

[0190] The power injection vector is the cooperative power allocation instruction set, where each component corresponds to the instantaneous power setting value of the battery module.

[0191] S5 maps the cooperative power allocation instruction set to a set of real-time cooperative control parameters for the LLC resonant converter, thereby driving the LLC resonant converter to perform differentiated and cooperative power management operations on different battery modules simultaneously.

[0192] The LLC resonant converter, as a multi-port output unit, has its power transmission capability for each port determined by the underlying electrical parameters.

[0193] A preferred implementation of mapping generation includes:

[0194] The real-time collaborative control parameter set includes the switching frequency of the primary power switch of the LLC resonant converter, the different bridge arms of the converter, and the switching commands for specific operating modes.

[0195] An internally pre-set power command control parameter mapping table is provided, and the control parameters are mapped to the actual parameters of the LLC resonant converter.

[0196] like Figure 3 As shown, in each control cycle, the power setpoints in the cooperative power allocation instruction set are combined with the current input voltage and the real-time measured values ​​of the voltage at each battery module terminal, and input into the mapping model for calculation. The calculation process outputs the optimal cooperative control parameters. For example, to achieve a larger current output to a certain port, a specific switching frequency and the phase offset that maximizes the effective duty cycle of the corresponding secondary winding of the port are calculated.

[0197] After generating the real-time collaborative control parameters, the collaborative control parameters are loaded into the digital pulse width modulation controller of the LLC resonant converter.

[0198] Because the control parameters can independently adjust the effective on-time or energy transfer window of each output circuit, even if all ports share the same resonant period, the converter can transfer different amounts of energy to different battery module ports within the same switching cycle, thereby simultaneously performing differentiated charging or discharging operations. For example, port A may be in a high-power charging state, while the adjacent port B may be in a low-power discharging or silent state.

[0199] The control parameters for all ports originate from the same collaborative optimization decision-making process and are uniformly generated through the same mapping model, ensuring that the power actions of each port are synchronized in time. For example, to reduce the charging current to module C while increasing the charging current to module D, the corresponding phase adjustments are complementary and coordinated, maintaining the overall soft-switching characteristics and operation of the converter.

[0200] The digital controller generates and updates multiple complementary PWM drive signals in real time based on the generated switching frequency and phase parameters. After being amplified by the drive circuit, the signals control the switching action of the power MOSFETs or IGBTs on the primary side of the LLC resonant converter, thereby realizing the differentiated and coordinated power flow management of each battery module by the coordinated power distribution instruction set.

[0201] Example 2.

[0202] like Figure 2 As shown, a modular battery control system using an LLC resonant converter includes:

[0203] The module consists of a state awareness module, a collaborative decision-making module, and a control module.

[0204] The state perception module includes a signal acquisition unit and a multivariate differential calculation unit. The signal acquisition unit is used to acquire the real-time state parameters of voltage, current and temperature of each battery module. The multivariate differential calculation unit is connected to the signal acquisition unit and is used to calculate, based on the real-time state parameters, a spatial gradient parameter reflecting the distribution of state differences between modules, a temporal differential parameter reflecting the trend of state changes of each module, and a cross-coupling parameter reflecting the degree of mutual influence between state parameters of different dimensions.

[0205] The collaborative decision-making module, connected to the state perception module, is used to perform multi-objective collaborative optimization decisions based on the multivariate differential relationship parameters. When there is a conflict between multiple control objectives, the collaborative decision-making module calculates the negative impact cost of a single control action on the associated objectives through the cross-coupling parameters, and dynamically determines the real-time priority of each control objective through the spatial gradient parameters and the time differential parameters, thereby finding a global compromise solution in the state space that enables the multivariate differential relationship parameters to evolve smoothly towards an equilibrium state.

[0206] The control module, connected to the collaborative optimization decision module and the LLC resonant converter, is used to demap the global compromise and generate a set of real-time collaborative control parameters for the LLC resonant converter, so as to drive the converter to perform differentiated and collaborative power management operations on different battery modules simultaneously.

[0207] It is important to note that the constructions and arrangements of this application shown in several different exemplary embodiments are merely illustrative. Although only two embodiments are described in detail in this disclosure, those who consult this disclosure will readily understand that many modifications are possible without substantially departing from the novel teachings and advantages of the subject matter described in this application. For example, variations in the size, dimensions, structure, shape, and proportions of various elements, as well as parameter values ​​(e.g., temperature, pressure, etc.), mounting arrangements, use of materials, color, orientation, etc. For instance, an element shown as integrally formed may be composed of multiple parts or elements, the position of elements may be inverted or otherwise altered, and the nature or number or position of discrete elements may be changed or altered. Therefore, all such modifications are intended to be included within the scope of this invention. The order or sequence of any process or method steps may be changed or rearranged according to alternative embodiments. Any "device plus function" clause is intended to cover the structure performing the function described herein, and not only structurally equivalent but also equivalent in structure. Other substitutions, modifications, alterations, and omissions may be made in the design, operation, and arrangement of the exemplary embodiments without departing from the scope of this invention. Therefore, the present invention is not limited to the specific embodiments, but extends to various modifications that still fall within the scope of the appended claims.

[0208] Furthermore, in order to provide a concise description of exemplary embodiments, not all features of actual embodiments (i.e., those features that are not relevant to the currently considered best mode for carrying out the invention, or those features that are not relevant to implementing the invention) may be omitted.

[0209] It should be understood that numerous specific implementation decisions can be made during the development of any practical implementation, such as in any engineering or design project. Such development efforts may be complex and time-consuming, but for those of ordinary skill in the art who benefit from this disclosure, the development effort will be a routine task in design, manufacturing, and production without requiring extensive experimentation.

[0210] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A modular battery control method based on an LLC resonant converter, characterized in that, include: S1 acquires the real-time status parameters of each of the multiple battery modules, including voltage, current and temperature; S2 calculates the multivariate differential relationship parameters of the interaction between the battery modules using the real-time state parameters of each battery module; The multivariate differential relation parameters are input into the optimization solver. Spatial gradient parameters are used to construct optimization objective terms regarding state consistency, time differential parameters are used to construct constraint terms regarding the dynamic response speed and stability of the system, and cross-coupling parameters are used to construct coupling cost terms describing the mutual constraints between different state variables. The optimization solver constructs a comprehensive cost function with optimization objective terms and coupling cost terms, and uses constraint terms as boundary conditions. By minimizing this comprehensive cost function, it solves for the optimal prediction vector of the power demand of each battery module within a future finite time window. The optimal prediction vector is decoupled and decomposed into a set of instantaneous power setting values ​​that correspond one-to-one with each battery module, are synchronized in time and coordinated in numerical value, based on the real-time state of each battery module and their interrelationship in the multivariate differential relation parameters, thus forming a collaborative power allocation instruction set. The instruction set is used to ensure that electrical connection and thermal constraints are met, and the power setpoint is used to implement the coordinated operation of optimal prediction vector planning and drive the synchronous response of each module. S3 uses the multivariate differential relationship parameters to perform multi-objective collaborative optimization decision-making and generate a collaborative power allocation instruction set corresponding to multiple battery modules; When there is a conflict between multiple control objectives, the negative impact cost of a single battery module on the associated objectives is calculated by cross-coupling parameters, and the real-time priority of each control objective is determined by spatial gradient parameters and time differential parameters. S4 uses the negative impact cost and the real-time priority to find a global compromise solution in the state space that enables the overall evolution of the multivariate differential relation parameters to a stable equilibrium state, which serves as the cooperative power allocation instruction set.

2. The modular battery control method based on an LLC resonant converter according to claim 1, characterized in that: The multivariate differential relation parameters include spatial gradient parameters reflecting the distribution of state differences between modules, temporal differential parameters reflecting the trend of state changes in each module, and cross-coupling parameters reflecting the degree of mutual influence between state parameters of different dimensions.

3. The modular battery control method based on an LLC resonant converter according to claim 2, characterized in that: A multi-dimensional state space is constructed using the real-time state parameters of all battery modules; The multidimensional state space obtains the spatial gradient parameter by calculating the ratio or statistical distribution characteristics of the changes in the same state parameter of any two different battery modules, which is used to quantify the spatial distribution pattern of state inconsistency. By performing time-dependent differential operations on the various state parameters of each battery module, the time differential parameter is obtained, which is used to quantify the instantaneous change rate and trend of the state of each module. The cross-coupling parameter is obtained by calculating the correlation ratio between the changes in at least two different dimensions of state parameters of any battery module, and is used to quantify the dynamic interaction strength between different physical quantities.

4. The modular battery control method based on an LLC resonant converter according to claim 1, characterized in that: Methods for calculating the cost of negative impacts include: S3011 takes the cross-coupling parameters, the expected control action for the target battery module, and the unit change in the first state parameter of the module caused by the control action as inputs; S3012 The unit change amount is used to calculate the predicted change amount of the second state parameter of the target battery module through the mapping relationship of the cross-coupling parameters; S3013 outputs the predicted change as the cost of the negative impact incurred when executing the control action to achieve the first objective. Methods for determining real-time priority include: S3021 determines the baseline priority of each control objective based on the state differences reflected by the spatial gradient parameters; S3022 dynamically adjusts the benchmark priority based on the rate of state change and acceleration trend indicated by the time differential parameter.

5. The modular battery control method based on an LLC resonant converter according to claim 1, characterized in that: Construct a Lyapunov function with the aforementioned multivariate differential relation parameters as key variables; The spatial gradient parameter defines the potential energy surface of the Lyapunov function, the cross-coupling parameter defines the curvature of the potential energy surface, and the time differential parameter is used to evaluate the instantaneous kinetic energy of the system's state trajectory. The global compromise solution is the state evolution direction selected in the energy field described by the Lyapunov function, which causes the sum of the system's potential energy and kinetic energy to decay the fastest and has the smoothest evolution path. The control input corresponding to this direction is the global compromise solution.

6. The modular battery control method based on an LLC resonant converter according to claim 5, characterized in that: The Lyapunov function embeds the quantized cost into a nonlinear constraint in the state space, transforming the dynamic priority into differentiated requirements for the convergence speed of different segments of the state trajectory. The global compromise solution is obtained by solving the constrained gradient flow optimization problem. The global compromise solution is used to enable the system state to adaptively bypass the obstacles formed by high-cost regions when moving along the negative gradient direction of the energy field, and to prioritize the convergence speed requirements corresponding to high-priority objectives.

7. The modular battery control method based on an LLC resonant converter according to claim 6, characterized in that: The global compromise solution is inversely solved by using a pre-set inverse system mapping model to obtain the power injection vector required to generate the current evolution direction under the current system constraints. The power injection vector is the cooperative power allocation instruction set, where each component corresponds to the instantaneous power setting value of the battery module.

8. A modular battery control system using an LLC resonant converter, for implementing the modular battery control method based on an LLC resonant converter as described in any one of claims 1-7, characterized in that, include: The module consists of a state awareness module, a collaborative decision-making module, and a control module. The state perception module includes a signal acquisition unit and a multivariate differential calculation unit; The signal acquisition unit is used to acquire the real-time state parameters of voltage, current and temperature of each battery module; the multivariate differential calculation unit is connected to the signal acquisition unit and is used to calculate, based on the real-time state parameters, spatial gradient parameters reflecting the distribution of state differences between modules, time differential parameters reflecting the trend of state changes of each module, and cross-coupling parameters reflecting the degree of mutual influence between state parameters of different dimensions. The collaborative decision-making module is used to make multi-objective collaborative optimization decisions based on the multivariate differential relationship parameters. When there is a conflict between multiple control objectives, the module calculates the negative impact cost of a single control action on the associated objectives through the cross-coupling parameters, and dynamically determines the real-time priority of each control objective through the spatial gradient parameters and time differential parameters, and finds a global compromise solution in the state space that enables the multivariate differential relationship parameters to evolve smoothly towards an equilibrium state. The control module is used to demap the global compromise and generate a set of real-time coordinated control parameters for the LLC resonant converter, so as to drive the converter to perform differentiated and coordinated power management operations on different battery modules simultaneously.

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