An adaptive battery modeling method for optical storage system dc bus voltage support

Abstract

The application relates to the technical field of battery modeling and photovoltaic energy storage systems, and discloses a self-adaptive battery modeling method for direct-current bus voltage support of a photovoltaic energy storage system. The method comprises the following steps: a basic model containing a direct-current voltage source, an ohmic internal resistance and an RC polarization network is established; the voltage drop depth is obtained by monitoring the direct-current bus voltage in real time, and is used for correcting the polarization resistance; then, the voltage is subjected to differential operation to obtain a change rate, so that the polarization capacitance is adjusted; the correction results of the two are combined, the battery model is dynamically optimized, and adaptive updating of the model parameters is realized; and the optimized model is applied to a photovoltaic energy storage integrated grid-connected system. The application improves the adaptability of the battery model to dynamic working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery modeling and photovoltaic energy storage system, and particularly relates to an adaptive battery modeling method for direct-current bus voltage support of a photovoltaic energy storage system. BACKGROUND

[0002] In a photovoltaic energy storage integrated grid-connected system, the transient stability of the battery is crucial for the safe operation of the system. In order to accurately predict the transient response of the system, the energy storage battery is usually mathematically modeled by an equivalent circuit model. The traditional first-order Thevenin equivalent circuit model has been widely used in this field, which is usually composed of an ideal direct-current voltage source, an ohmic internal resistance and an RC network composed of a polarization resistance and a polarization capacitance in parallel. This model effectively simplifies the complex characteristics of the battery, enabling transient analysis in photovoltaic energy storage systems. However, the traditional Thevenin model has some limitations, especially during the transient process of voltage drop or severe fluctuations in the system, the fixed parameter setting cannot accurately reflect the actual behavior of the battery under extreme conditions.

[0003] For example, when the direct-current bus voltage drops sharply, the physical and chemical properties of the real battery will change dramatically, and its polarization resistance will instantaneously decrease, thereby providing greater instantaneous current to support the bus voltage. However, the polarization resistance in the traditional model is a fixed value, which cannot simulate this dynamic change, resulting in the model underestimating the voltage support capability of the battery. In addition, when the voltage fluctuates severely, the capacitance effect of the battery also changes, playing an important damping role to alleviate the impact of voltage fluctuations. However, due to the static setting of the polarization capacitance, the traditional model cannot dynamically enhance this damping effect, which may lead to incorrect simulation results predicting system instability.

[0004] Therefore, it is an urgent problem to provide an adaptive battery modeling method for direct-current bus voltage support of a photovoltaic energy storage system to dynamically adjust the battery model parameters during voltage fluctuations and faults, and more realistically simulate the response characteristics of the battery under extreme working conditions. SUMMARY

[0005] The present application provides an adaptive battery modeling method for direct-current bus voltage support of a photovoltaic energy storage system, which improves the adaptability of the battery model to dynamic working conditions.

[0006] In a first aspect, the present application provides an adaptive battery modeling method for direct-current bus voltage support of a photovoltaic energy storage system, which comprises:

[0007] A first-order Thevenin equivalent circuit model is established to obtain a battery model, the battery model comprising an ideal DC voltage source, an ohmic internal resistance, and an RC network connected in series, the RC network being formed by a polarization resistance and a polarization capacitance connected in parallel;

[0008] The DC bus voltage is monitored in real time to obtain a voltage drop depth, and a polarization resistance adjustment value is obtained according to the voltage drop depth;

[0009] The DC bus voltage is differentiated to obtain a voltage change rate of the DC bus, and a polarization capacitance adjustment value is obtained based on the voltage change rate;

[0010] The battery model is adjusted based on the polarization resistance adjustment value and the polarization capacitance adjustment value to obtain an adaptive optimization battery model;

[0011] The adaptive optimization battery model is applied to a grid-connected system of a photovoltaic storage integrated system to obtain more accurate battery simulation results.

[0012] Optionally, a mathematical relationship between the polarization resistance and the voltage drop depth is: (1), in formula (1), is a polarization resistance value at time t, is a normal polarization resistance reference value, is a polarization coefficient, is a DC bus voltage value at time t, is a system rated DC bus voltage.

[0013] Optionally, a mathematical relationship between the polarization capacitance and the voltage change rate is: , in formula (2), is a polarization capacitance value at time t, is a static polarization capacitance reference value, is a gain coefficient, is a trigger threshold for distinguishing bus fluctuations from normal changes, is a DC bus voltage value at time t, is the voltage change rate.

[0014] Optionally, the adaptive adjustment of the polarization resistance acts on the current support capability of the battery, and the polarization resistance decreases when the DC bus voltage value drops.

[0015] Optionally, the adaptive adjustment of the polarization capacitance acts on the damping characteristic of the battery, and the polarization capacitance increases when the DC bus voltage fluctuates sharply.

[0016] Optionally, the battery model dynamically adjusts the polarization resistance and the polarization capacitance by monitoring the DC bus voltage in real time and calculating the voltage change rate, to realize the support of the battery for transient stability in the photovoltaic energy storage DC grid-connected system.

[0017] Optionally, the dynamic stability analysis of the adaptive optimization battery model in the battery management system (BMS) is realized by allocating more than 4 kB of continuous RAM space in the memory area of the BMS for state vector parameter storage, and making the state vector parameters continuously reside and update in real time in the RAM space, the state vector parameters including polarization resistance, polarization capacitance, polarization voltage, state of charge, phase angle margin and gain margin.

[0018] Optionally, the BMS dynamically estimates the polarization resistance and the polarization capacitance of the battery by an extended Kalman filter algorithm, and estimates the polarization voltage and the state of charge in real time; when the stable margin calculation result shows that the system damping is insufficient, the BMS automatically adjusts the active damping coefficient, and sends a power reduction instruction to the inverter through a communication protocol.

[0019] Optionally, when the polarization resistance reaches the amplitude limiting boundary for 5 consecutive periods, the BMS immediately freezes the adaptive update, reverts to the initial parameter set, reports a fault code 0x0B to the upper computer, and re-executes the extended Kalman filter covariance reset and parameter alignment within 100 ms after the communication is restored.

[0020] Optionally, the battery model can be built not only with a first-order Thevenin equivalent circuit model, but also with a second-order or higher-order RC equivalent circuit model.

[0021] In the technical scheme provided in the application, an adaptive adjustment mechanism is introduced to realize dynamic optimization of the battery model in the integrated photovoltaic energy storage grid-connected system. Specifically, the polarization resistance and the polarization capacitance of the battery are no longer fixed constants, but are dynamically adjusted according to the real-time changes of the DC bus voltage. This scheme can effectively solve the problem that the existing technology cannot accurately simulate the response of the battery in voltage drop and severe fluctuations, especially when the system fails or the load is disturbed, the current support capacity and damping characteristics of the battery are improved. By monitoring the voltage change in real time and adaptively adjusting the battery model parameters, this scheme improves the transient stability and simulation accuracy of the system, avoids the problem of underestimating the performance of the battery caused by fixed parameters in the traditional model, and thus provides a more accurate basis for stability evaluation and control strategy optimization of the integrated photovoltaic energy storage grid-connected system. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0023] Figure 1 A flowchart of an adaptive battery modeling method for DC bus voltage support of a light storage system according to the present application;

[0024] Figure 2 A first-order Thevenin equivalent circuit structure diagram of an adaptive battery modeling system for DC bus voltage support of light storage grid connection according to the present application;

[0025] Figure 3 A flowchart of a polarization resistance adaptive adjustment mechanism of an adaptive battery modeling device for DC bus voltage support of light storage grid connection according to the present application. DETAILED DESCRIPTION

[0026] The embodiment of the present application provides a method for adaptive battery modeling of DC bus voltage support of light storage grid connection. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] For the convenience of understanding, the specific process of the embodiment of the present application will be described below. Please refer to Figure 1 One embodiment of the adaptive battery modeling method for DC bus voltage support of light storage grid connection in the embodiment of the present application comprises the following steps.

[0028] Step S1, a first-order Thevenin equivalent circuit model is established to obtain a battery model, the battery model comprises an ideal DC voltage source, an ohmic internal resistance, and an RC network connected in series, and the RC network is formed by a polarization resistance and a polarization capacitance connected in parallel.

[0029] Specifically, by establishing a first-order Thevenin equivalent circuit model, a battery model capable of truly reflecting the dynamic characteristics of the battery is constructed. The model is composed of an ideal DC voltage source, an ohmic internal resistance, and an RC network connected in series, where the RC network is formed by a polarization resistance and a polarization capacitance in parallel, which is used to depict the polarization effect and buffer characteristics of the battery during charging and discharging. The ideal DC voltage source is used to represent the open-circuit voltage of the battery at a given state of charge, the ohmic internal resistance is used to simulate the ohmic loss inside the battery, and the polarization resistance and polarization capacitance jointly reflect the electrochemical polarization and concentration polarization behavior of the battery in the transient process, thereby accurately modeling the dynamic changes of the battery terminal voltage under different operating conditions. In the traditional method, these parameters are usually set as static constants, which is difficult to reflect the real response of the battery when the voltage drops or fluctuates sharply. A parameter adaptive adjustment mechanism is further introduced to dynamically correct the values of the polarization resistance and the polarization capacitance by real-time monitoring of the DC bus voltage and its rate of change, so that the battery model not only maintains good simulation accuracy in steady-state conditions, but also better simulates the voltage support and damping characteristics of the battery during sudden disturbances and rapid fluctuations, thereby improving the transient stability analysis effect of the photovoltaic energy storage direct-current grid-connected system. Figure 2 The figure shows a first-order Thevenin equivalent circuit structure diagram, which contains E0, R b , R p , C p The core difference and improvement is that the polarization resistance R p and the polarization capacitance C p are no longer fixed values, but can be dynamically and adaptively adjusted according to the real-time state of the DC bus voltage U dc .

[0030] Step S2, real-time monitoring of the DC bus voltage, obtaining the voltage drop depth, and obtaining the polarization resistance adjustment value according to the drop depth.

[0031] Specifically, by real-time monitoring of the change of the DC bus voltage during operation, the difference between the current voltage and the rated reference voltage is calculated to obtain the depth of the voltage drop, and the drop depth is used as an important basis for adjusting the polarization resistance. When the DC bus voltage is maintained near the rated value, the polarization resistance remains unchanged at the reference value; when the bus voltage drops significantly, the larger the drop amplitude, the greater the adjustment amplitude of the polarization resistance, so that the polarization resistance value gradually decreases to reflect the dynamic characteristics of the battery releasing larger instantaneous current by reducing the internal resistance when the voltage drops sharply.

[0032] Step S3, differential operation of the DC bus voltage to obtain the voltage change rate of the DC bus, and based on the voltage change rate to obtain the polarization capacitance adjustment value.

[0033] Specifically, by discretely differentiating the DC bus voltage, the rate of change of the voltage over time is obtained, and a denoising and limiting amplitude link (such as a moving average and a first-order lag) is added in the front end to suppress measurement noise and spike interference; then the voltage rate of change is compared with the set trigger threshold value, when the rate of change is in the normal fluctuation interval, only the polarization capacitance is slightly adjusted or not adjusted; when the rate of change increases and exceeds the threshold value, the target value of the polarization capacitance is increased according to the predetermined gain law, so as to reflect the enhancement of the capacitance effect in the fast oscillation scene, thereby prolonging the transient time constant of the RC equivalent network, improving the absorption and buffering capacity of the high-frequency component of the bus voltage, and realizing active damping of voltage oscillation. In order to avoid numerical instability caused by excessive regulation, upper and lower limits and hysteresis bands are also set, and a smooth back mechanism is used to gradually restore the polarization capacitance to the reference level after the disturbance subsides, ensuring that the model response is both sensitive and robust.

[0034] Step S4, based on the polarization resistance adjustment value and the polarization capacitance adjustment value, adjusting the battery model to obtain an adaptive optimization battery model.

[0035] Specifically, the polarization resistance adjustment value obtained according to the voltage drop depth and the polarization capacitance adjustment value obtained according to the voltage rate of change are injected into the equivalent circuit parameter set at the same time, the RC branch and the port equation are recalibrated in real time, and the parameter upper and lower limits and the hysteresis band are set to avoid chattering and excessive correction; in each sampling period, denoising and limiting amplitude are performed first, then parameter smoothing transition and anti-saturation processing are completed, so that the polarization resistance and the polarization capacitance are updated gradually according to the predetermined dynamic law; the terminal voltage and the branch current are solved according to the updated parameters, and a more sensitive and more robust response to the transient process is obtained. The model updated through the above closed loop can constitute an adaptive optimization battery model, which can improve the simulation accuracy and numerical stability of the DC bus voltage support and oscillation damping.

[0036] Step S5, applying the adaptive optimization battery model to the photovoltaic energy storage DC grid-connected system to obtain more accurate battery simulation results.

[0037] Specifically, the adaptive optimization battery model is integrated into the simulation environment of the photovoltaic energy storage direct current grid-connected system in the form of programmable components, a data interaction interface is established with the battery management system (BMS) and the converter controller (such as DC / DC and DC / AC), real-time DC bus voltage and current measurements are received, and equivalent terminal voltage and branch current are output; in the scenarios of fault ride-through, load mutation, rapid fluctuation of illumination, etc., the model updates the polarization resistance and polarization capacitance online according to the voltage drop depth and voltage change rate, thereby strengthening the instantaneous current support and oscillation damping capability. By linking the DC bus, battery cluster and control link in system-level simulation, the key indicators such as bus voltage drop amplitude, recovery time, overshoot and steady-state error can be more accurately reproduced, and the reliability of transient stability evaluation and control strategy setting can be improved; compared with the fixed parameter model, the method shows better numerical stability and consistency in multiple working conditions, which is helpful to guide the optimization of capacity configuration, protection setting and power allocation strategy, and obtain more accurate battery simulation results.

[0038] It can be understood that the execution subject of the present application can be a direct current bus voltage support adaptive battery modeling system of grid-connected light storage, and can also be a terminal or a server, which is not limited here. The server is taken as an example for description in the embodiments of the present application.

[0039] In a specific embodiment, the mathematical relationship between the polarization resistance and the voltage drop depth is: (1),

[0040] In formula (1), is the polarization resistance value at time t, is the normal polarization resistance reference value, is the polarization coefficient, is the DC bus voltage value at time t, is the system rated DC bus voltage. For example, assuming that the rated DC bus voltage of the photovoltaic energy storage system is 1500V, the normal polarization resistance reference value of the battery is 0.135 mΩ, and the polarization coefficient is 0.5. During system operation, the DC bus voltage has dropped from 1500V to 1200V. At this time, the depth of voltage drop is 20% (i.e. the voltage drop is 300V). According to the voltage drop depth, the polarization resistance value is dynamically adjusted. When the bus voltage drops to 1200V, the polarization resistance is automatically reduced according to the set adjustment coefficient to reflect the ability of the battery to release larger instantaneous current when the voltage drops suddenly. After adjustment, the polarization resistance is reduced from 0.135 mΩ to 0.1215 mΩ.

[0041] In a specific embodiment, the mathematical relationship between the polarization capacitance and the voltage change rate is: ,

[0042] In formula (2), is the polarization capacitance value at time t, is the static polarization capacitance reference value, is the gain coefficient, is the trigger threshold value to distinguish bus fluctuations from normal changes, is the DC bus voltage value at time t, is the voltage change rate. For example, in a specific embodiment, assuming that the static polarization capacitance reference value of the system is 2258.2 F, the gain coefficient is 0.3, and the trigger threshold value is 30 V / ms. The change rate of the DC bus voltage at a certain time is 50 V / ms, which exceeds the set threshold value. According to the voltage change rate, the polarization capacitance will be adaptively adjusted. When the voltage change rate exceeds the threshold value, the polarization capacitance will be increased according to the set gain coefficient, enhancing the damping characteristics of the battery to better buffer voltage fluctuations. In this example, the value of the polarization capacitance will be increased from the reference value of 2258.2 F to 15807.4 F, about 7 times.

[0043] Specifically, the adaptive adjustment mechanism of the polarization resistance enhances the current support capability of the battery during DC bus voltage drop by dynamically changing the internal resistance of the battery. When the DC bus voltage drops, the physical and chemical properties of the battery change dramatically, and the polarization resistance needs to be adjusted according to the depth of voltage drop, so that the battery can provide more current in a short time to support voltage recovery. Specifically, when the bus voltage drops, the polarization resistance value of the battery will automatically decrease according to the depth of voltage drop. The deeper the drop, the greater the reduction in polarization resistance. This adjustment enables the battery to better cope with voltage drops, releasing more instantaneous current by reducing internal resistance, enhancing the battery's support for voltage drops. Compared with the fixed polarization resistance value in the traditional model, this adaptive adjustment mechanism can more realistically simulate the dynamic characteristics of the battery during voltage drop, effectively improving the stability and response speed of the system during sudden voltage drop. Figure 3 The figure shows the polarization resistance adaptive adjustment mechanism flow.

[0044] For example, in a photovoltaic energy storage system, the system's nominal DC bus voltage is 1500V, the battery's normal polarization resistance reference value is 0.135 mΩ, and the static polarization capacitance reference value is 2500F. When the system fails, the DC bus voltage drops from 1500V to 1200V, with a voltage drop depth of 20%. The system monitors the voltage drop in real time and adjusts the polarization resistance according to the drop depth. According to the adaptive adjustment mechanism, the larger the voltage drop, the smaller the polarization resistance, simulating the battery releasing a larger instantaneous current to support voltage recovery when the voltage drops suddenly. After adjustment, the polarization resistance is reduced from 0.135 mΩ to 0.1215 mΩ. At the same time, the system continues to monitor the voltage rate of change and calculates it in real time. Assuming the voltage rate of change is 45V / ms, which exceeds the set trigger threshold of 30V / ms, the system immediately increases the polarization capacitance dynamically. Through the adjustment of the gain coefficient, the capacitance value is increased from 2500F to 3500F to enhance the damping characteristics of the battery and help absorb voltage fluctuations and alleviate oscillation. As the voltage fluctuation stabilizes, the polarization capacitance gradually returns to its static reference value.

[0045] For example, in a photovoltaic energy storage system, the system's nominal DC bus voltage is 1500V, the battery's normal polarization resistance reference value is 0.135 mΩ, and the static polarization capacitance reference value is 2500F. When the system fails, the DC bus voltage drops from 1500V to 1200V, with a voltage drop depth of 20%. The system monitors the voltage drop in real time and adjusts the polarization resistance according to the drop depth. According to the adaptive adjustment mechanism, the larger the voltage drop, the smaller the polarization resistance, simulating the battery releasing a larger instantaneous current to support voltage recovery when the voltage drops suddenly. After adjustment, the polarization resistance is reduced from 0.135 mΩ to 0.1215 mΩ. At the same time, the system continues to monitor the voltage rate of change and calculates it in real time. Assuming the voltage rate of change is 45V / ms, which exceeds the set trigger threshold of 30V / ms, the system immediately increases the polarization capacitance dynamically. Through the adjustment of the gain coefficient, the capacitance value is increased from 2500F to 3500F to enhance the damping characteristics of the battery and help absorb voltage fluctuations and alleviate oscillation. As the voltage fluctuation stabilizes, the polarization capacitance gradually returns to its static reference value.

[0046] In a photovoltaic energy storage system, the battery management system needs to analyze and optimize the dynamic stability of the battery in real time. The memory area of the BMS is allocated with more than 4 kB of continuous RAM space at one time to store and manage key state vector parameters. These state vector parameters include polarization resistance, polarization capacitance, polarization voltage, state of charge, phase angle margin, and gain margin, which are crucial for battery performance evaluation and system stability. By allocating sufficient memory space, the BMS can ensure that these parameters are continuously resident and updated in RAM, enabling accurate monitoring and adjustment of battery status.

[0047] In practical applications, the BMS continuously updates these state vector parameters based on real-time battery data such as voltage, current, temperature, etc., ensuring that the system can dynamically adjust the control strategy according to the current state of the battery. For example, when the voltage of the battery fluctuates abnormally, the BMS adjusts the dynamic response of the battery by updating the polarization capacitance and polarization resistance in real time, enhancing the voltage support capability and damping characteristics of the battery. In addition, the BMS optimizes the charging and discharging process and stability of the system through real-time updates of the state of charge and gain margin, ensuring the safe operation of the battery under different working conditions.

[0048] For example, in a photovoltaic energy storage system, the battery management system dynamically estimates the polarization resistance and polarization capacitance of the battery through the extended Kalman filter algorithm. The extended Kalman filter algorithm combines the input and output signals of the battery to adjust these key parameters in the battery model in real time, thereby estimating the polarization voltage and state of charge of the battery. Through this method, the BMS can update the polarization voltage and state of charge of the battery in real time according to the voltage and current changes of the battery, ensuring that the battery management system accurately monitors the health status and charging and discharging process of the battery.

[0049] When the BMS finds that the system damping is insufficient through stability margin analysis, and the capacitive effect of the battery cannot effectively alleviate voltage fluctuations, the system will automatically adjust the active damping coefficient to enhance the damping characteristics of the battery. This adjustment mechanism enables the battery to better respond to voltage fluctuations and prevents the system from experiencing sustained oscillation or instability. The BMS will also issue a power reduction command to the inverter through the communication protocol to reduce the power output of the system, thereby reducing the load on the battery and helping the system recover to stability.

[0050] Taking a certain photovoltaic energy storage system as an example, during the operation of the system, the BMS monitors that the polarization resistance of the battery reaches the set amplitude boundary for five consecutive periods (assuming each period is 1 second), indicating that the electrochemical characteristics of the battery have abnormally fluctuated. The BMS immediately freezes the adaptive update process of the polarization resistance and resets all parameters of the battery model to the initial set value to prevent the system from experiencing incorrect dynamic response. In addition, the BMS reports fault code 0x0B to the upper computer, indicating that the battery is in an abnormal state. At the same time, since the communication recovery of the system requires a certain time, the BMS re-executes the covariance reset and parameter alignment operations in the extended Kalman filter algorithm within 100 milliseconds after the communication is restored. This process helps the BMS recalibrate the state estimation of the battery, ensuring that the battery management system can accurately estimate the polarization resistance, polarization capacitance, and other key parameters of the battery after the system resumes normal operation, preventing the system from experiencing more serious failures due to parameter mismatch. Through this emergency mechanism, the system can automatically correct and recover when the battery parameters are abnormal, ensuring the safe and stable operation of the photovoltaic energy storage system.

[0051] Specifically, the battery management system in the system not only uses a first-order Thevenin equivalent circuit model to construct a battery model, but also can obtain a more accurate battery behavior simulation through a second-order or higher-order RC equivalent circuit model as needed. Specifically, when the system faces more complex working environments or transient operating conditions, the BMS will select to use a second-order RC circuit model containing two RC branches according to the dynamic response requirements of the battery, to better simulate the electrochemical reaction and energy transfer characteristics of the battery during charging and discharging. The second-order RC model can provide more refined time constants and voltage change responses, more accurately capturing the current support capacity and damping characteristics of the battery, especially when the voltage fluctuates sharply or the system fails. Through this flexible model selection, the BMS can dynamically adjust the complexity of the battery model according to different working conditions, ensuring that the system can always run under the most suitable model, further improving the stability and efficiency of the photovoltaic energy storage system.

[0052] The above-described embodiments are merely used to illustrate the technical solutions of the present application, but not limit the same; even though the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An adaptive battery modeling method for optical storage system DC bus voltage support, characterized in that, The method comprises: S1, a first-order Thevenin equivalent circuit model is established to obtain a battery model, the battery model comprises an ideal DC voltage source, an ohmic internal resistance, and an RC network connected in series, and the RC network is formed by a polarization resistance and a polarization capacitance connected in parallel; S2, the DC bus voltage is monitored in real time to obtain a voltage drop depth, and a polarization resistance adjustment value is obtained according to the voltage drop depth; S3, a differential operation is performed on the DC bus voltage to obtain a voltage change rate of the DC bus, and a polarization capacitance adjustment value is obtained based on the voltage change rate; S4, the battery model is adjusted based on the polarization resistance adjustment value and the polarization capacitance adjustment value to obtain an adaptive optimization battery model; S5, the adaptive optimization battery model is applied to a photovoltaic energy storage DC grid-connected system to obtain more accurate battery simulation results.

2. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 1, wherein, The mathematical relationship between the polarization resistance and the voltage drop depth is: (1), In formula (1), is the polarization resistance value at time t, is the normal polarization resistance reference value, is the polarization coefficient, is the DC bus voltage value at time t, is the system rated DC bus voltage.

3. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 1, wherein, The mathematical relationship between the polarization capacitance and the voltage rate of change is: , In formula (2), is the polarization capacitance value at time t, is the static polarization capacitance reference value, is the gain coefficient, is the trigger threshold value for distinguishing bus fluctuations from normal changes, is the DC bus voltage value at time t, is the voltage change rate.

4. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 2, wherein, The adaptive adjustment of the polarization resistance acts on the current support capability of the battery, and the polarization resistance decreases when the DC bus voltage value drops.

5. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 3, wherein, The adaptive adjustment of the polarization capacitance acts on the damping characteristic of the battery, and the polarization capacitance increases when the DC bus voltage fluctuates sharply.

6. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 1, wherein, The battery model dynamically adjusts the polarization resistance and the polarization capacitance by monitoring the DC bus voltage in real time and calculating the voltage change rate, thereby realizing the support of the battery for transient stability in the photovoltaic energy storage integrated grid-connected system.

7. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 1, wherein, The dynamic stability analysis of the adaptive optimization battery model in the battery management system (BMS) is realized by the following method: allocating more than 4 kB of continuous RAM space in the memory area used by the BMS for state of charge estimation at one time, storing state vector parameters, and making the state vector parameters continuously reside and update in real time in the RAM space, the state vector parameters including polarization resistance, polarization capacitance, polarization voltage, state of charge, phase angle margin and gain margin.

8. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 7, wherein, The BMS dynamically estimates the polarization resistance and the polarization capacitance of the battery by extending the Kalman filter algorithm, and estimates the polarization voltage and the state of charge in real time. When the stable margin calculation result shows that the system damping is insufficient, the BMS will automatically adjust the active damping coefficient and send a power reduction instruction to the inverter through the communication protocol.

9. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 7, wherein, When the polarization resistance reaches the amplitude limiting boundary for 5 consecutive periods, the BMS immediately freezes the adaptive update, reverts to the initial parameter set, reports fault code 0x0B to the upper computer, and re-executes the extended Kalman filter covariance reset and parameter alignment within 100 ms after the communication is restored.

10. The adaptive battery modeling method for optical storage system DC bus voltage support of claim 1, wherein, The battery model can not only be built by a first-order Thevenin equivalent circuit model, but also by a second-order or higher-order RC equivalent circuit model.

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