A control method and control system for a lithium flow battery based on silicon / graphite slurry electrodes

Abstract

The present application relates to a kind of control method and control system of lithium flow battery based on silicon / graphite slurry electrode, control method includes: S1.data is collected by sensor array;S2.based on the data collected, the internal state of battery is calculated by multi-physics field coupling model;S3.based on model calculation result, slurry state monitoring algorithm is executed, and slurry settlement state is judged;S4.according to the internal state of battery and slurry settlement state, adaptive control decision is executed, and charge-discharge mode is switched and slurry flow rate is adjusted;S5.based on real-time data, the parameters of multi-physics field coupling model are updated by parameter online identification algorithm;Wherein, multi-physics field coupling model includes electrochemical model, fluid dynamics model and thermodynamics model, electrochemical model introduces silicon particle volume expansion effect correction factor and graphite interlayer diffusion restriction coefficient.The present application can effectively predict the performance of the battery under different working conditions, significantly improve system cycle life and energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of electrochemical energy storage technology, specifically to a control method and control system for a lithium redox flow battery based on silicon / graphite slurry electrodes. Background Technology

[0002] As a crucial technology for large-scale energy storage, the performance of lithium-ion flow batteries largely depends on the choice of electrode materials. Silicon and graphite, as two of the most promising anode materials, exhibit unique advantages in slurry electrode lithium-ion flow batteries, but also bring new technical challenges.

[0003] While silicon-based slurry electrodes possess a theoretical specific capacity as high as 4200 mAh / g, they suffer from severe volume expansion (approximately 300%) during cycling, leading to: changes in rheological properties, increasing pumping power; particle breakage and repeated SEI film growth, accelerating capacity decay; and decreased electrode structural stability, affecting cycle life. Graphite-based slurry electrodes exhibit a stable layered structure and a moderate specific capacity (372 mAh / g), but suffer from: slow interlayer lithium-ion diffusion kinetics, limiting rate performance; easy orientation and alignment of sheet particles during flow, affecting reaction uniformity; and susceptibility to lithium plating during overcharging, posing a safety hazard.

[0004] Existing flow battery models are mostly based on aqueous systems and cannot accurately describe the unique electrochemical behavior of silicon / graphite slurry electrodes in organic electrolyte systems. In particular, existing methods have significant shortcomings in predicting multiphysics coupling effects and long-term performance degradation. Therefore, there is an urgent need in this field to develop a precise modeling and intelligent control system specifically for silicon / graphite slurry electrode lithium flow batteries. Summary of the Invention

[0005] Based on the above description, the present invention provides a control method and control system for lithium redox flow batteries based on silicon / graphite slurry electrodes, aiming to improve the accuracy of model prediction for silicon / graphite slurry electrodes.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: This invention provides a control method for a lithium redox flow battery based on a silicon / graphite slurry electrode, characterized in that it includes: S1. Data acquisition steps: Collect battery voltage, current, temperature, slurry flow rate and pressure data through sensor array; S2. Multiphysics Modeling Steps: Based on the data collected in step S1, the internal state of the battery is calculated using a multiphysics coupling model. S3. Condition monitoring steps: Execute the slurry condition monitoring algorithm based on the model calculation results to determine the slurry settling state; S4. Control Decision Step: Based on the internal state of the battery in step S2 and the sedimentation state of the slurry in step S3, execute adaptive control decision, automatically switch charging and discharging modes and adjust slurry flow rate; S5. Parameter update steps: Based on real-time data, the parameters of the multiphysics coupling model are updated online using an online parameter identification algorithm; The multiphysics coupling model in step S2 includes an electrochemical model, a fluid dynamics model, and a thermodynamic model. The electrochemical model incorporates a silicon particle volume expansion effect correction factor and a graphite interlayer diffusion restriction coefficient.

[0007] Furthermore, the multiphysics coupling model also includes a particle dynamics sub-model, which is used to describe the motion, distribution, and sedimentation behavior of active particles in the slurry.

[0008] Furthermore, the expression for the silicon particle volume expansion effect correction factor is shown in equation (1). (1), Where α-Si is the silicon particle volume expansion effect correction factor, β is the expansion coefficient, γ is the attenuation factor, ΔV / V0 is the relative volume change rate, and SOC is the state of charge.

[0009] Furthermore, the value of β ranges from 0.15 to 0.25, and the value of γ ranges from 0.1 to 0.3.

[0010] Furthermore, the expression for the interlayer diffusion confinement coefficient of graphite is shown in equation (2). (2), Where ξ_Gr is the interlayer diffusion confinement coefficient of graphite, η is the diffusion confinement coefficient, κ is the rate constant, and I / I_ref is the relative current density.

[0011] Furthermore, the value of η ranges from 0.1 to 0.2, and the value of κ ranges from 0.5 to 1.0.

[0012] Furthermore, in step S4, the adaptive control decision includes: (1) Constant current-constant voltage switching charging mode, which automatically switches when the voltage reaches the set threshold; (2) Variable flow rate control strategy, which adjusts the slurry flow rate according to the state of charge and temperature of the battery; (3) Pulse maintenance mode: Apply pulse current periodically to alleviate electrode aging.

[0013] Furthermore, in step S4, the voltage setting threshold is 3.5 V-4.5 V; When the charge state is below 30% and the temperature is below 35℃, adjust the slurry flow rate to 1 L / min-2 L / min; The pulse current is 0.5 C-3 C, the pulse width is 10 ms-500 ms, and the pulse period is 10-100 charge-discharge cycles or 24 h-240 h.

[0014] Furthermore, in step S5, the online parameter identification algorithm includes recursive least squares or extended Kalman filter algorithm.

[0015] This invention also proposes a multiphysics modeling and intelligent control system for lithium redox batteries based on silicon / graphite slurry electrodes, comprising: A sensor array is used to collect data on battery voltage, current, temperature, slurry flow rate, and pressure. A processor, communicatively connected to the sensor array, is used to execute the control method for a lithium flow battery based on silicon / graphite slurry electrodes as described above.

[0016] Compared with the prior art, the technical solution of this application has the following beneficial technical effects: 1. This invention introduces a material property correction factor to construct an electrochemical model that can accurately describe the special behavior of silicon / graphite slurry electrodes. The model predicts voltage with small error and high accuracy compared to measured values. 2. The adaptive control strategy of the present invention can effectively alleviate the volume expansion damage of silicon particles and the diffusion restriction of graphite by adjusting the charging and discharging mode and slurry flow rate in a coordinated manner. Experiments have shown that it can significantly improve the cycle life of the battery system. 3. The online parameter identification module of this system enables the model to adapt to battery aging, maintain the accuracy of long-term control, and improve the management efficiency of the system throughout its entire life cycle; 4. This invention deeply integrates multiphysics modeling with intelligent control, resulting in a clear system structure, strong practicality, applicability to silicon / graphite slurry electrode systems with different formulations and structures, and ease of engineering application and promotion. Attached Figure Description

[0017] Figure 1 The structure diagram of the intelligent control system provided by the present invention; Figure 2 A schematic diagram of the multiphysics coupling model framework provided by this invention; Figure 3 The flowchart of silicon adaptive control and online parameter identification provided by this invention; Figure 4 The silicon-optimized flow rate decision-making flowchart provided by this invention; Figure 5 The graphite optimization flow rate decision-making flowchart provided by the present invention; Figure 6 This is a schematic diagram of the online parameter identification module provided by the present invention. Detailed Implementation

[0018] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0020] As a crucial technology for large-scale energy storage, the performance of lithium-ion flow batteries largely depends on the choice of electrode materials. Silicon and graphite, as two of the most promising anode materials, exhibit unique advantages in slurry electrode lithium-ion flow batteries, but also bring new technical challenges.

[0021] While silicon-based slurry electrodes possess a theoretical specific capacity as high as 4200 mAh / g, they suffer from severe volume expansion (approximately 300%) during cycling, leading to: changes in rheological properties, increasing pumping power; particle breakage and repeated SEI film growth, accelerating capacity decay; and decreased electrode structural stability, affecting cycle life. Graphite-based slurry electrodes exhibit a stable layered structure and a moderate specific capacity (372 mAh / g), but suffer from: slow interlayer lithium-ion diffusion kinetics, limiting rate performance; easy orientation and alignment of sheet particles during flow, affecting reaction uniformity; and susceptibility to lithium plating during overcharging, posing a safety hazard.

[0022] Existing flow battery models are mostly based on aqueous systems and cannot accurately describe the unique electrochemical behavior of silicon / graphite slurry electrodes in organic electrolyte systems. In particular, existing methods have significant shortcomings in predicting multiphysics coupling effects and long-cycle performance degradation. Therefore, there is an urgent need in this field to develop a precise modeling and intelligent control system specifically for silicon / graphite slurry electrode lithium flow batteries. This system needs to be able to: 1) accurately describe the unique effects such as silicon volume expansion and graphite interlayer diffusion through multiphysics coupling models; 2) monitor and predict changes in the physical state of the slurry (such as sedimentation) in real time; and 3) implement adaptive control strategies (such as adjusting flow rate and switching charge / discharge modes) based on model predictions and state monitoring to simultaneously optimize the battery's energy efficiency, rate performance, and extend its cycle life.

[0023] In view of this, see Figure 1 This invention provides a control method for a lithium redox flow battery based on a silicon / graphite slurry electrode, characterized by comprising: S1. Data acquisition steps: Collect battery voltage, current, temperature, slurry flow rate and pressure data through sensor array; S2. Multiphysics Modeling Steps: Based on the data collected in step S1, the internal state of the battery is calculated using a multiphysics coupling model. S3. Condition monitoring steps: Execute the slurry condition monitoring algorithm based on the model calculation results to determine the slurry settling state; S4. Control Decision Step: Based on the internal state of the battery in step S2 and the sedimentation state of the slurry in step S3, execute adaptive control decision, automatically switch charging and discharging modes and adjust slurry flow rate; S5. Parameter update steps: Based on real-time data, the parameters of the multiphysics coupling model are updated online using an online parameter identification algorithm; The multiphysics coupling model in step S2 includes an electrochemical model, a fluid dynamics model, and a thermodynamic model. The electrochemical model incorporates a silicon particle volume expansion effect correction factor and a graphite interlayer diffusion restriction coefficient.

[0024] In the technical solution of this invention, an electrochemical model capable of accurately describing the special behavior of silicon / graphite slurry electrodes is constructed by introducing a material property correction factor. The model predicts voltage with small errors compared to measured values, demonstrating high accuracy. The adaptive control strategy, through coordinated adjustment of charge / discharge modes and slurry flow rate, effectively alleviates the volume expansion damage of silicon particles and the diffusion limitation of graphite, and experiments have shown that it can significantly improve the cycle life of the battery system. The online parameter identification module enables the model to adapt to battery aging, maintain long-term control accuracy, and improve the management efficiency of the system throughout its entire life cycle. This invention deeply integrates multiphysics modeling with intelligent control, resulting in a clear system structure, strong practicality, applicability to silicon / graphite slurry electrode systems with different formulations and structures, and ease of engineering application and promotion.

[0025] In this invention, by constructing a multi-physics coupled model that integrates electrochemistry, fluid dynamics, and thermodynamics, and by introducing a silicon particle volume expansion effect correction factor and a graphite interlayer diffusion restriction coefficient into the electrochemical sub-model, it is possible to accurately describe the structural evolution and interfacial side reactions caused by the huge volume change of the silicon anode during charging and discharging, as well as the lithium-ion intercalation dynamics of the graphite anode, thereby overcoming the problem of insufficient applicability of traditional aqueous flow battery models in organic electrolyte systems.

[0026] For details, please refer to Figure 1In some embodiments of the present invention, a sensor layer is set up to collect data on battery voltage, current, temperature, slurry flow rate and pressure; a state estimation layer is set up to estimate SOC (state of charge) and SOH (state of health) based on the collected data, and to determine whether a fault exists; a control decision layer is set up to select a control decision based on the estimated judgment; and an execution layer is set up to output a control signal according to the selected control decision.

[0027] For further details, please refer to [link / reference]. Figure 2 The multiphysics coupling model also includes a particle dynamics sub-model, which is used to describe the motion, distribution and sedimentation behavior of active particles in the slurry.

[0028] In the technical solution of this invention, a particle dynamics sub-model is further introduced into the multiphysics coupling model, enhancing the ability to describe and predict the complex physical behavior inside the slurry electrode of lithium-ion flow batteries. This particle dynamics sub-model can simulate and track the three-dimensional motion trajectory, concentration distribution evolution, and sedimentation dynamics of active material (such as silicon or graphite) particles in organic electrolyte slurry in real time, fully considering the comprehensive influence of key factors such as particle size, density, shape, surface charge, and flow field shear on slurry stability. Furthermore, the accurate perception and modeling of particle sedimentation states provides crucial input for online parameter identification and lifetime prediction, enabling the entire control system to possess stronger environmental adaptability and aging robustness. In summary, this technology not only improves the operational reliability and cycle life of lithium-ion flow batteries at high energy densities (especially silicon-based systems), but also lays the theoretical and methodological foundation for the engineering design and intelligent operation and maintenance of slurry-based energy storage systems.

[0029] Furthermore, the expression for the silicon particle volume expansion effect correction factor is shown in equation (1). (1), Where α-Si is the silicon particle volume expansion effect correction factor, β is the expansion coefficient, γ is the attenuation factor, ΔV / V0 is the relative volume change rate, and SOC is the state of charge.

[0030] In the technical solution of this invention, by introducing a silicon particle volume expansion effect correction factor, the nonlinear volume change characteristics ΔV / V0, charge state dependence, and expansion inhibition effect caused by cyclic aging of silicon materials during the lithiation / delithiation process are comprehensively considered. This enables the electrochemical sub-model to more realistically reflect key physicochemical processes such as porosity changes, electron / ion conduction path reconstruction, interface stress accumulation, and SEI film evolution caused by volume expansion during actual operation.

[0031] Furthermore, the value of β ranges from 0.15 to 0.25, and the value of γ ranges from 0.1 to 0.3.

[0032] In the technical solution of the present invention, by limiting the range of the expansion coefficient β to 0.15-0.25 and the range of the attenuation factor γ to 0.1-0.3, the engineering applicability and prediction stability of the multiphysics coupling model are significantly improved while ensuring the physical rationality of the silicon particle volume expansion effect correction factor.

[0033] This parameter range is based on a large amount of experimental data and calibration of the actual lithiation behavior of silicon materials in organic electrolytes. It can accurately reflect the magnitude of volume change of typical nano-silicon or composite silicon-based materials during cycling and its nonlinear decay trend with the evolution of the state of charge.

[0034] Furthermore, the expression for the interlayer diffusion confinement coefficient of graphite is shown in equation (2). (2), Where ξ_Gr is the interlayer diffusion confinement coefficient of graphite, η is the diffusion confinement coefficient, κ is the rate constant, and I / I_ref is the relative current density.

[0035] In the technical solution of this invention, by introducing the interlayer diffusion restriction coefficient of graphite, the nonlinear behavior of lithium-ion insertion kinetics of graphite-based slurry electrodes under different charge-discharge rates is revealed, which significantly improves the prediction accuracy of electrochemical models for concentration polarization and reaction inhomogeneity under high current density conditions.

[0036] This expression, based on the hyperbolic tangent function, smoothly and continuously reflects the inhibitory effect of relative current density I / I_ref on the diffusion ability of lithium ions between graphite layers: when the current density is low, ξ_Gr is close to 1, indicating that the diffusion resistance is negligible and the reaction kinetics are close to ideal; as the current density increases, tanh(κ·I / I_ref) approaches 1, and ξ_Gr decreases to 1. η exhibits a clear diffusion restriction effect.

[0037] Furthermore, the value of η ranges from 0.1 to 0.2, and the value of κ ranges from 0.5 to 1.0.

[0038] In the technical solution of this invention, by limiting the parameters η∈[0.1,0.2] and κ∈[0.5, 1.0] in the graphite interlayer diffusion restriction coefficient model, this invention significantly improves the quantitative accuracy and engineering applicability of the model for the dynamic behavior of graphite-based slurry electrodes under actual operating conditions while ensuring physical accuracy.

[0039] The diffusion limitation coefficient η controls the maximum degree of diffusion inhibition (i.e., the minimum sustainable level of the reaction rate at high rates). If η is too small (<0.1), the model underestimates the lithium intercalation resistance of graphite at high current densities, easily leading to the neglect of lithium plating risk; if η is too large (>0.2), the charging and discharging capacity is overly conservatively limited, reducing the system power utilization. Limiting η to the range of 0.1-0.2 accurately reflects the reasonable diffusion bottleneck caused by the confined interlayer channels of typical artificial graphite or modified graphite particles in organic electrolyte systems.

[0040] The rate constant κ determines the steepness of the diffusion limitation response to the increase in relative current density (I / I_ref). If the value of κ is too small (<0.5), the model will be sluggish in response to current changes and will be unable to capture the dynamic deterioration at the beginning of the rate jump in time; if κ is too large (>1.0), it will cause abrupt changes in the correction coefficients, which may lead to oscillations in the control strategy. Limiting κ to the range of 0.5-1.0 ensures that ξ_Gr smoothly and sensitively reflects the gradual development of diffusion limitation within the commonly used operating current range, which is in high agreement with the experimentally observed trend of polarization voltage growth.

[0041] Furthermore, in step S4, the adaptive control decision includes: (1) Constant current-constant voltage switching charging mode, which automatically switches when the voltage reaches the set threshold; (2) Variable flow rate control strategy, which adjusts the slurry flow rate according to the state of charge and temperature of the battery; (3) Pulse maintenance mode: Apply pulse current periodically to alleviate electrode aging.

[0042] In the technical solution of the present invention, a multi-dimensional adaptive control decision mechanism including constant current-constant voltage switching charging, variable flow rate control and pulse maintenance mode is introduced in step S4, which significantly improves the operational safety, energy efficiency and cycle life of silicon / graphite slurry electrode lithium flow battery under complex working conditions.

[0043] The constant current-constant voltage switching charging mode employs a constant current mode in the initial charging stage to improve charging efficiency. When the battery terminal voltage approaches a set threshold, it automatically switches to a constant voltage mode, effectively suppressing the risk of overcharging, slowing down the excessive growth of the SEI film, and avoiding structural damage caused by the accelerated silicon volume expansion under high SOC. This strategy balances charging speed and electrode stability, making it particularly suitable for high specific capacitance silicon-based systems.

[0044] Variable flow rate control strategy: The slurry pump flow rate is dynamically adjusted based on real-time state of charge and temperature. In the high SOC or high temperature range, the flow rate is appropriately increased to enhance particle suspension, improve mass transfer uniformity, and strengthen heat dissipation, preventing local concentration polarization and heat accumulation. In the low SOC or low temperature range, the flow rate is reduced to decrease pump power loss and improve the overall system energy efficiency. This strategy effectively addresses flow-induced non-uniform reactions such as silicon particle sedimentation and graphite sheet orientation, ensuring the consistency of electrode interface reactions.

[0045] Pulse Maintenance Mode: During long-term operation or rest, short-duration, reverse, or low-amplitude pulsed currents are applied periodically to disturb the electrode / electrolyte interface, promoting lithium-ion redistribution, mitigating concentration gradient accumulation, and helping to repair some passivated interfaces or strip unstable SEI byproducts. This mode can effectively delay the pulverization failure of silicon electrodes and the lithium dendrite formation of graphite electrodes, significantly suppressing capacity decay and extending battery cycle life.

[0046] Furthermore, in step S4, the voltage setting threshold is 3.5 V-4.5 V; When the charge state is below 30% and the temperature is below 35℃, adjust the slurry flow rate to 1 L / min-2 L / min; The pulse current is 0.5 C-3 C, the pulse width is 10 ms-500 ms, and the pulse period is 10-100 charge-discharge cycles or 24 h-240 h.

[0047] In the technical solution of this invention, by adjusting the voltage setting threshold to 3.5 V-4.5 V, the charging efficiency and interface stability can be effectively balanced, especially suppressing the accelerated silicon volume expansion and continuous SEI film growth under high SOC. The upper limit of 4.5 V avoids excessive oxidation and decomposition of the electrolyte and degradation of the positive electrode structure, while the lower limit of 3.5 V ensures that lithium can be fully intercalated in the constant voltage stage without triggering the violent expansion of silicon or graphite lithium deposition.

[0048] By limiting the flow rate to 1 L / min-2 L / min, it is possible to maintain sufficient particle suspension, prevent reaction dead zones caused by bottom deposition, and avoid unnecessary pump power loss caused by excessively high flow rates.

[0049] By adjusting the pulse maintenance parameters, the set pulse current intensity covers a range from mild disturbance to strong interface activation, the pulse width takes into account both ion relaxation time and electrode stress tolerance, and the period setting matches the aging rate of the silicon / graphite system. Regularly applying such pulses can effectively disperse local lithium concentration gradients, alleviate concentration polarization, promote the repair or remodeling of microcracks in the SEI film, inhibit lithium dendrite nucleation, and, especially on the graphite surface, activate silicon particles that have become inactive due to sedimentation or agglomeration.

[0050] Furthermore, in step S5, the online parameter identification algorithm includes recursive least squares or extended Kalman filter algorithm.

[0051] In the technical solution of the present invention, recursive least squares (RLS) or extended Kalman filter (EKF) is used as the online parameter identification algorithm in step S5, which significantly improves the adaptability, robustness and long-term prediction accuracy of the multiphysics coupling model in actual operation.

[0052] When using RLS, the system can efficiently update model parameters with low computational complexity based on real-time collected input-output data such as voltage, current, temperature, and flow rate, making it particularly suitable for the rapid identification of linear or approximately linear subsystems. When using EKF, it can handle the nonlinear dynamics that are common in multiphysics models and can still achieve the joint optimal estimation of hidden states and parameters under noise interference.

[0053] This invention also proposes a multiphysics modeling and intelligent control system for lithium redox batteries based on silicon / graphite slurry electrodes, comprising: A sensor array is used to collect data on battery voltage, current, temperature, slurry flow rate, and pressure. A processor, communicatively connected to the sensor array, is used to execute the control method for a lithium flow battery based on silicon / graphite slurry electrodes as described above.

[0054] In the technical solution of this invention, the sensor array can synchronously and in real-time acquire key physical quantities such as voltage, current, temperature, slurry flow rate, and pipeline pressure, covering multiple dimensions of electrochemistry, thermodynamics, and fluid dynamics, providing high-quality input data for multiphysics coupling models. A dedicated processor, communicatively connected to the sensor array, embeds the aforementioned complete control method logic, including core modules such as multiphysics modeling, slurry state monitoring, adaptive control decision-making, and online parameter identification. This processor can drive high-fidelity model operation based on real-time data, dynamically calculate the unmeasurable internal states of the battery, and accordingly execute intelligent strategies such as constant current-constant voltage switching, variable flow rate adjustment, and pulse maintenance, forming a closed-loop control link of "perception—modeling—decision-execution—learning."

[0055] The technical solution of the present invention will be further described in detail below with reference to specific embodiments. It should be understood that the following embodiments are only used to explain the present invention and are not intended to limit the present invention.

[0056] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0057] Example 1 This embodiment provides a control method for a lithium flow battery based on silicon / graphite slurry electrodes. The specific application and effects in a silicon-based slurry electrode lithium flow battery system are as follows: (1) System configuration and modeling: Configure a single lithium redox flow battery with a rated capacity of 5 Ah. The negative electrode slurry consists of the following components: 35% by mass of silicon particles (D50=8 μm), 3% by mass of conductive agent (Super P), 2% by mass of binder (polyacrylic acid), and 60% by mass of electrolyte (1M LiPF6 in EC / DEC).

[0058] The system operation process strictly follows Figures 3 to 5 The logic shown in the flowchart (Adaptive Flow Control and Online Parameter Identification) is as follows. The construction of the multiphysics coupling model follows... Figure 2 The framework shown integrates sub-models of electrochemistry, fluid dynamics, thermodynamics, and particle dynamics.

[0059] (2) Model parameter setting Silicon electrode correction parameters The volume expansion coefficient β = 0.18 SOC attenuation factor γ = 0.15 The kinetic attenuation coefficient k_d = 0.02 Adaptive flow rate control parameters Baseline flow rate v_base = 1.0 (L / min) Maximum charging voltage V_charge_max = 3.8 (V) Low SOC threshold: SOC_low = 0.3 High temperature threshold T_high = 40 (°C) (3) Control process and results: After system initialization, such as Figure 1 As shown, the sensor layer begins to collect data, and the state estimation layer performs SOC estimation.

[0060] Control decision-making level Figure 3 and Figure 4 The process is as follows: when SOC < 30% and temperature T < 35℃, the instruction actuator layer increases the slurry flow rate to 1.2 L / min; if the algorithm determines that the risk of settling is high at the same time, the flow rate is further increased to 1.5 L / min.

[0061] When the voltage reaches 3.8V, the system automatically switches from constant current charging to constant voltage charging mode.

[0062] Online parameter identification module (its process is shown in...) Figure 3 See structure Figure 4It works continuously throughout the process, updating parameters such as the model's internal resistance based on the RLS algorithm.

[0063] Testing revealed that the system achieved an initial discharge specific capacity of 1850 mAh / g (based on silicon mass) at 0.5C rate, with an average energy efficiency of 92%. After 100 cycles, the capacity retention reached 85%, demonstrating that the system effectively manages silicon-based slurry electrodes and significantly mitigates capacity decay.

[0064] Example 2 This embodiment details the specific application and effects of the present invention in a graphite-based slurry electrode lithium redox flow battery system as follows: (1) System Configuration and Modeling: A single lithium redox flow battery with a rated capacity of 10 Ah was configured. The negative electrode slurry consisted of the following components: 40% by mass of graphite particles (D50=15 μm), 2% by mass of conductive agent (Ketjen Black), and 58% by mass of electrolyte (1M LiPF6 in EC / DMC). The overall operation and information flow of the system strictly followed... Figure 2 The hierarchical structure shown in the (Intelligent Control System Structure Diagram) is as follows.

[0065] (2) Model parameter settings: Graphite electrode correction parameters The diffusion confinement coefficient η = 0.15 Rate constant κ = 0.8 Control parameters Maximum charging voltage V_charge_max = 4.2 (V) Baseline flow rate v_base = 1.5 (L / min) Low SOC threshold: SOC_low = 0.3 High temperature threshold T_high = 40 (°C) The core of this system is to optimize the interlayer diffusion problem of graphite electrodes. For example... Figure 3 and Figure 5 As shown in the process flow diagram, when the SOC > 50%, the control decision layer increases the flow rate to 1.8 L / min to enhance mass transfer.

[0066] The multiphysics coupling model (framework see below) Figure 2 The rate performance variation of graphite electrodes under different flow rates was accurately captured by the ξ_Gr coefficient.

[0067] Through precise pump control at the actuator layer, the system achieves smooth adjustment of the slurry flow rate. Test results show that the system maintains a capacity retention rate (relative to 0.5C) of 98% at a 1C charge-discharge rate. After 300 cycles, the overall capacity retention rate reaches 94%, and the energy efficiency remains stable at 95%, fully verifying the invention's ability to efficiently manage and maintain the long lifespan of graphite-based slurry electrodes.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0069] In summary, the technical solution of this application has the following beneficial technical effects: 1. This invention introduces a material property correction factor to construct an electrochemical model that can accurately describe the special behavior of silicon / graphite slurry electrodes. The model predicts voltage with small error and high accuracy compared to measured values. 2. The adaptive control strategy of the present invention can effectively alleviate the volume expansion damage of silicon particles and the diffusion restriction of graphite by adjusting the charging and discharging mode and slurry flow rate in a coordinated manner. Experiments have shown that it can significantly improve the cycle life of the battery system. 3. The online parameter identification module of this system enables the model to adapt to battery aging, maintain the accuracy of long-term control, and improve the management efficiency of the system throughout its entire life cycle; 4. This invention deeply integrates multiphysics modeling with intelligent control, resulting in a clear system structure, strong practicality, applicability to silicon / graphite slurry electrode systems with different formulations and structures, and ease of engineering application and promotion.

Claims

1. A control method for a lithium redox flow battery based on silicon / graphite slurry electrodes, characterized in that, include: S1. Data acquisition steps: Collect battery voltage, current, temperature, slurry flow rate and pressure data through sensor array; S2. Multiphysics Modeling Steps: Based on the data collected in step S1, the internal state of the battery is calculated using a multiphysics coupling model. S3. Condition monitoring steps: Execute the slurry condition monitoring algorithm based on the model calculation results to determine the slurry settling state; S4. Control Decision Step: Based on the internal state of the battery in step S2 and the sedimentation state of the slurry in step S3, execute adaptive control decision, automatically switch charging and discharging modes and adjust slurry flow rate; S5. Parameter update steps: Based on real-time data, the parameters of the multiphysics coupling model are updated online using an online parameter identification algorithm; The multiphysics coupling model in step S2 includes an electrochemical model, a fluid dynamics model, and a thermodynamic model. The electrochemical model incorporates a silicon particle volume expansion effect correction factor and a graphite interlayer diffusion restriction coefficient.

2. The control method for a lithium redox flow battery based on a silicon / graphite slurry electrode according to claim 1, characterized in that, The multiphysics coupling model also includes a particle dynamics sub-model, which is used to describe the motion, distribution and sedimentation behavior of active particles in the slurry.

3. The control method for a lithium redox flow battery based on a silicon / graphite slurry electrode according to claim 1, characterized in that, The expression for the silicon particle volume expansion effect correction factor is shown in equation (1). （1）， Where α-Si is the silicon particle volume expansion effect correction factor, β is the expansion coefficient, γ is the attenuation factor, ΔV / V0 is the relative volume change rate, and SOC is the state of charge.

4. The control method for a lithium redox flow battery based on a silicon / graphite slurry electrode according to claim 3, characterized in that, The value of β ranges from 0.15 to 0.25, and the value of γ ranges from 0.1 to 0.

3.

5. The control method for a lithium redox flow battery based on a silicon / graphite slurry electrode according to claim 1, characterized in that, The expression for the interlayer diffusion confinement coefficient of graphite is shown in equation (2). （2）， Where ξ_Gr is the interlayer diffusion confinement coefficient of graphite, η is the diffusion confinement coefficient, κ is the rate constant, and I / I_ref is the relative current density.

6. The control method for a lithium redox flow battery based on a silicon / graphite slurry electrode according to claim 5, characterized in that, The value of η ranges from 0.1 to 0.2, and the value of κ ranges from 0.5 to 1.

0.

7. The control method for a lithium redox flow battery based on a silicon / graphite slurry electrode according to claim 1, characterized in that, In step S4, the adaptive control decision includes: (1) Constant current-constant voltage switching charging mode, which automatically switches when the voltage reaches the set threshold; (2) Variable flow rate control strategy, which adjusts the slurry flow rate according to the state of charge and temperature of the battery; (3) Pulse maintenance mode: Apply pulse current periodically to alleviate electrode aging.

8. The control method for a lithium redox flow battery based on a silicon / graphite slurry electrode according to claim 7, characterized in that, In step S4, the voltage setting threshold is 3.5 V-4.5 V; When the charge state is below 30% and the temperature is below 35℃, adjust the slurry flow rate to 1 L / min-2 L / min; The pulse current is 0.5 C-3 C, the pulse width is 10 ms-500 ms, and the pulse period is 10-100 charge-discharge cycles or 24 h-240 h.

9. The control method for a lithium redox flow battery based on a silicon / graphite slurry electrode according to claim 1, characterized in that, In step S5, the online parameter identification algorithm includes recursive least squares or extended Kalman filter algorithm.

10. A multiphysics modeling and intelligent control system for lithium redox flow batteries based on silicon / graphite slurry electrodes, characterized in that, include: A sensor array is used to collect data on battery voltage, current, temperature, slurry flow rate, and pressure. A processor, communicatively connected to the sensor array, is configured to execute the control method for a lithium flow battery based on silicon / graphite slurry electrodes as described in any one of claims 1 to 9.

Images