Photovoltaic grid-connected high-efficiency energy storage transmission system

Abstract

The invention discloses a photovoltaic grid-connected high-efficiency energy storage transmission system, and relates to the field of energy storage system management. Comprising a data acquisition module for synchronously acquiring terminal voltage, loop current and temperature of each battery pack based on a sensing device; the state estimation module is used for determining internal state parameters including the charge state, the health state and the internal resistance based on the collected data; the health assessment module is used for calculating health stress tolerance based on the health state, the internal resistance change trend and the temperature history; the boundary calculation module is used for determining the maximum safe charging and discharging power boundary based on the current charge state and the temperature; the power distribution module is used for generating an optimal power distribution instruction based on the health stress tolerance and the power boundary; and the power control module issues an instruction to the power converter and controls each battery pack to operate according to specified power so as to realize photovoltaic grid-connected energy storage. Through health state management and multi-objective optimization, the full life cycle efficiency and operation reliability of the energy storage system are improved under the condition of ensuring safe operation.

Description

Technical Field

[0001] This invention relates to the field of energy storage system management, specifically to a photovoltaic grid-connected high-efficiency energy storage and transmission system. Background Technology

[0002] With the rapid development and widespread application of photovoltaic power generation technology, configuring large-scale battery energy storage systems has become a key technological path to improve the grid connection stability of photovoltaic power plants, realize energy time-shifting, and enhance absorption capacity. In actual operation, power plant-scale energy storage systems typically consist of hundreds of battery packs connected in series and parallel. Due to differences in manufacturing processes, operating environments, and usage history, the performance parameters and health status of each battery pack will gradually become inconsistent. Existing energy storage systems, when allocating power, usually aim to ensure the overall output of the system, employing strategies based on the current state of charge of the battery packs or simple polling. However, such methods do not fully consider the differences in the health status between battery packs and the dynamic changes in their degradation rates. Under long-term operation, battery packs with poor health status may experience accelerated degradation due to power stress that is not matched to their actual tolerance capacity, thus becoming a bottleneck restricting the lifespan of the entire system, increasing the system's operation and maintenance costs, and affecting the economic benefits throughout its entire life cycle.

[0003] Therefore, how to maximize the overall service life of the energy storage system by actively balancing the degradation rate of each battery pack through a more intelligent energy management strategy, while ensuring system safety and meeting grid-connected power requirements, is an important issue that urgently needs optimization. Summary of the Invention

[0004] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide a high-efficiency photovoltaic grid-connected energy storage and transmission system to solve the aforementioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a photovoltaic grid-connected high-efficiency energy storage and transmission system, comprising:

[0006] Data acquisition module: used to synchronously acquire the terminal voltage, loop current and temperature of each battery pack based on the sensing devices corresponding to each battery pack;

[0007] State estimation module: used to determine the internal state parameters of each battery pack based on terminal voltage, loop current and temperature, the internal state parameters including state of charge, state of health and internal resistance;

[0008] Health assessment module: used to calculate the health stress tolerance of each battery pack based on its health status, historical trend of internal resistance, and historical temperature data;

[0009] Boundary calculation module: used to determine the maximum safe charge and discharge power boundary of each battery pack based on the current state of charge and temperature of each battery pack;

[0010] Power allocation module: used to generate optimal power allocation instructions for each battery pack based on health stress tolerance and maximum safe charge and discharge power boundaries;

[0011] Power control module: Used to send the optimal power allocation command to the power converter corresponding to each battery pack, control each battery pack to perform charging and discharging operations according to the specified power, and realize the grid-connected transmission of photovoltaic energy.

[0012] The present invention is further configured such that the data acquisition module includes:

[0013] A synchronization signal is generated in each control cycle to trigger the synchronous acquisition of terminal voltage, loop current and temperature of each battery pack.

[0014] The acquired raw terminal voltage, loop current, and temperature data are digitally filtered to suppress high-frequency noise and remove outliers, and the pre-processed terminal voltage, loop current, and temperature are output.

[0015] The present invention is further configured such that the state estimation module includes:

[0016] The state of charge of each battery pack is estimated by using the ampere-hour integral method and fusing the extended Kalman filter algorithm.

[0017] By analyzing complete charge-discharge cycle data of the battery pack, the health status of each battery pack can be estimated.

[0018] The internal resistance of each battery pack is calculated based on the dynamic response of the terminal voltage and loop current of the battery pack.

[0019] The present invention is further configured such that the health assessment module includes:

[0020] The basic health component is calculated based on the ratio of the battery pack's current health status to its initial health status.

[0021] The internal resistance change trend factor is calculated based on the relative rate of change of the battery pack internal resistance and the average internal resistance within a preset historical window.

[0022] The temperature stress history factor is calculated based on the cumulative operating time of the battery pack within the preset high temperature range.

[0023] The health stress tolerance of each battery pack is obtained by multiplying the basic health component, the internal resistance change trend factor, and the temperature stress history factor. The health stress tolerance is negatively correlated with the internal resistance change trend factor and the temperature stress history factor.

[0024] The present invention is further configured such that the boundary calculation module includes:

[0025] Based on the real-time state of charge and temperature of each battery pack, combined with the preset electrochemical characteristics of the battery, the instantaneous maximum allowable charging power boundary and the maximum allowable discharging power boundary of the battery pack are determined.

[0026] The instantaneous maximum allowable charging power boundary and the maximum allowable discharging power boundary together constitute the maximum safe charging and discharging power boundary of the battery pack.

[0027] The present invention is further configured such that the power distribution module includes:

[0028] A system power allocation optimization model is constructed, in which the power allocation value of each battery pack is set as the optimization variable, the total power demand of the system is set as the equality constraint, and the maximum safe charge and discharge power boundary and the state of charge safety window of each battery pack are set as the inequality constraint.

[0029] Define the objective function of the system power allocation optimization model. The objective function contains two terms: the first term is the sum of the aging cost terms of each battery pack, wherein the aging cost term of each battery pack is composed of the ratio of the square of its power allocation value to the square of its health stress tolerance; the second term is the largest single aging cost term among all battery packs.

[0030] A convex optimization algorithm is used to solve the system power allocation optimization model. By minimizing the objective function and obtaining the optimal solution that satisfies all constraints, the optimal power allocation command for each battery pack is generated.

[0031] The present invention is further configured such that the power control module includes:

[0032] The optimal power allocation command is sent to the power converter corresponding to each battery pack.

[0033] Based on the optimal power allocation command, a pulse width modulation signal is generated through a current closed-loop control algorithm, and this signal is used to drive the power converter to achieve precise control of the charging and discharging current of each battery pack.

[0034] The present invention is further configured such that the current closed-loop control algorithm calculates and generates the duty cycle of the pulse width modulation signal used to drive the power switching devices in the power converter based on the deviation between the optimal power allocation command and the actual output power of the battery pack.

[0035] The present invention is further configured such that the system also includes a visualization module for displaying the internal state parameters, health stress tolerance, and optimal power allocation instructions of each battery pack in real time.

[0036] This invention provides a high-efficiency photovoltaic grid-connected energy storage and transmission system. It comprises a data acquisition module for synchronously acquiring the terminal voltage, loop current, and temperature of each battery pack based on sensors corresponding to each battery pack; a state estimation module for determining the internal state parameters of each battery pack based on the terminal voltage, loop current, and temperature, including state of charge, health state, and internal resistance; a health assessment module for calculating the health stress tolerance of each battery pack based on its health state, historical trends in internal resistance, and historical temperature data; a boundary calculation module for determining the maximum safe charge / discharge power boundary of each battery pack based on its current state of charge and temperature; a power allocation module for generating optimal power allocation instructions for each battery pack based on the health stress tolerance and the maximum safe charge / discharge power boundary; and a power control module for issuing the optimal power allocation instructions to the power converters corresponding to each battery pack, controlling each battery pack to perform charge / discharge operations according to the specified power, thereby achieving grid-connected transmission of photovoltaic energy. The beneficial effects include:

[0037] 1. By introducing the health stress tolerance, which comprehensively reflects the rate of deterioration of battery internal resistance and historical temperature stress, as the core optimization weight, the system can actively avoid applying excessive stress to battery packs with poor health when allocating power, while encouraging battery packs with good health to take on more work, effectively slowing down the degradation rate of weak battery packs, thereby extending the service life of the entire battery energy storage system and reducing replacement and maintenance costs.

[0038] 2. By calculating the maximum safe charge and discharge power boundary of each battery pack in real time and using it as a hard constraint for optimization, it ensures that any power allocation command is executed within the absolute safe operating range of the battery, fundamentally preventing safety problems caused by overcharging, over-discharging, and overheating. At the same time, the optimization model pursues the minimization of the overall aging cost of the system within the safety boundary, achieving the optimal efficiency under the premise of absolute safety.

[0039] 3. The convex optimization algorithm is used to solve the power distribution problem, which ensures the existence, uniqueness and computational efficiency of the optimal solution and can meet the requirements of real-time system control. The maximum single aging cost term introduced in the objective function reflects the principle of minimizing the maximum pressure, which helps to prevent the overload of a single battery pack and avoids the risk of cascading failures caused by a single battery pack reaching its limit, thereby improving the robustness and power supply reliability of the overall system.

[0040] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying 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. In the drawings:

[0042] Figure 1 This is a structural diagram of a photovoltaic grid-connected high-efficiency energy storage and transmission system, which is an exemplary embodiment of the present invention. Detailed Implementation

[0043] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0044] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0045] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0046] A high-efficiency photovoltaic grid-connected energy storage and transmission system, such as Figure 1 As shown, it includes:

[0047] Data acquisition module: used to synchronously acquire the terminal voltage, loop current and temperature of each battery pack based on the sensing devices corresponding to each battery pack;

[0048] State estimation module: used to determine the internal state parameters of each battery pack based on terminal voltage, loop current and temperature, the internal state parameters including state of charge, state of health and internal resistance;

[0049] Health assessment module: used to calculate the health stress tolerance of each battery pack based on its health status, historical trend of internal resistance, and historical temperature data;

[0050] Boundary calculation module: used to determine the maximum safe charge and discharge power boundary of each battery pack based on the current state of charge and temperature of each battery pack;

[0051] Power allocation module: used to generate optimal power allocation instructions for each battery pack based on health stress tolerance and maximum safe charge and discharge power boundaries;

[0052] Power control module: Used to send the optimal power allocation command to the power converter corresponding to each battery pack, control each battery pack to perform charging and discharging operations according to the specified power, and realize the grid-connected transmission of photovoltaic energy.

[0053] The present invention is further configured such that the data acquisition module includes:

[0054] A synchronization signal is generated in each control cycle to trigger the synchronous acquisition of terminal voltage, loop current and temperature of each battery pack.

[0055] The acquired raw terminal voltage, loop current, and temperature data are digitally filtered to suppress high-frequency noise and remove outliers, outputting pre-processed terminal voltage, loop current, and temperature. Specifically, the synchronous acquisition process of the data acquisition module is implemented through a synchronization signal generator based on a high-stability clock source. A precision timer is set in the main controller to generate a reference clock signal at a fixed period. This signal is processed by a phase-locked loop to generate a highly accurate synchronization pulse. The synchronization pulse is broadcast to all data acquisition subunits via a dedicated synchronization bus. Each acquisition subunit has a built-in delay compensation circuit that can automatically calculate and compensate for signal transmission delay based on its physical location in the communication link. The subsequent trigger signal ensures that the time synchronization error of all acquisition units remains at an extremely low level. Once the synchronization trigger signal arrives effectively, each acquisition subunit executes multi-parameter acquisition tasks in parallel according to a preset process. The voltage sampling channel starts first, adjusting the battery pack terminal voltage to the range of the analog-to-digital converter (ADC) through a differential amplifier circuit. Then, a high-precision ADC completes the digital conversion, employing oversampling technology to improve effective resolution. The current sampling channel starts synchronously; the voltage signal output from the magnetic field sensor is converted by an independent ADC after passing through an isolation amplifier. To eliminate measurement deviations caused by ambient temperature changes, the system reads the sensor's internal temperature data in real time for online compensation. The temperature acquisition channel then... Using a time-division multiplexing method, the measured values ​​of multiple temperature sensors are read sequentially. The physical quantities are converted into temperature values ​​using a lookup table method, and their average value is calculated. This average value is used as the final temperature measurement result for the battery pack. Multiple digital filtering processes are applied to the collected raw terminal voltage, loop current, and temperature data to improve data quality. The first-stage filter uses a finite-length unit impulse response filter, which suppresses high-frequency noise while preserving the effective components of the signal by preset an appropriate cutoff frequency. Its filter coefficients are designed using the window function method to ensure linear phase characteristics. The second-stage filter uses a sliding weighted average algorithm. This algorithm calculates the average value of the current data window and dynamically adjusts its weights based on the deviation of each data point from the average value, automatically assigning... Data points with large deviations are given lower weights, thereby smoothing the signal while effectively suppressing impulse interference and improving the robustness and accuracy of signal processing. In the preprocessing and quality assurance stage, timestamp alignment is first performed by maintaining a global clock and synchronizing each acquisition unit to ensure that the terminal voltage, loop current, and temperature data acquired at the same time have an accurate time correspondence. Data with deviations exceeding a preset threshold are resynchronized. Secondly, the sensor cross-validation function is used to evaluate the rationality of the data by analyzing the inherent physical relationship between terminal voltage, loop current, and temperature. For example, it verifies whether the voltage rises during charging and whether the voltage drops during discharging. Data combinations that violate basic physical laws are marked as suspicious data.For identified abnormal or suspicious data, the data compensation function is activated, prioritizing direct replacement with data from the previous valid period. If anomalies persist, an extrapolation algorithm based on recent historical data trends is used to generate replacement values, thereby ensuring the continuity and dynamic rationality of the data flow.

[0056] The present invention is further configured such that the state estimation module includes:

[0057] The state of charge of each battery pack is estimated by using the ampere-hour integral method and fusing the extended Kalman filter algorithm.

[0058] By analyzing complete charge-discharge cycle data of the battery pack, the health status of each battery pack can be estimated.

[0059] Based on the dynamic response of the battery pack's terminal voltage and loop current, the internal resistance of each battery pack is calculated. Specifically, the estimation of the state of charge (SOC) employs a dual-correction fusion mechanism based on the ampere-hour integral method and the extended Kalman filter algorithm. First, the ampere-hour integral method is performed. A high-precision current sensor continuously monitors the charging and discharging current of the battery pack, and the current is integrated over time to obtain the net change in charge. This change is algebraically added to the SOC estimate from the previous cycle to obtain the preliminary SOC value based on the integral method. Simultaneously, based on a pre-established battery equivalent circuit model, the extended Kalman filter algorithm is used for recursive SOC estimation. This process includes two alternating stages: prediction and correction. In the prediction stage, based on the SOC estimate from the previous time step... The electrical state estimate and current measurement are used to predict the current state of charge (SPO) and terminal voltage by using a state equation describing the relationship between charge conservation and electrochemical kinetics. During the correction phase, the predicted terminal voltage is compared with the actual measured terminal voltage to obtain information. This information is then weighted using a Kalman gain matrix to correct the predicted SPO. This gain matrix is ​​dynamically adjusted based on the statistical characteristics of model prediction and measurement errors, thereby enhancing the correction weight when the measurement value is reliable and reducing its impact when measurement noise is significant. The preliminary SPO calculated based on the ampere-hour integral method is then fused with the SPO output from the extended Kalman filter algorithm after measurement correction using a weighted average to form the final SPO. State estimation; the battery equivalent circuit model aims to simulate the electrical characteristics of the battery's external ports using simple circuit elements. This model can be implemented by constructing a voltage source, an ohmic internal resistance, and one or two sets of polarized branches consisting of resistors and capacitors in parallel. The health state, reflecting the degree of battery capacity degradation, is estimated by analyzing complete charge-discharge cycle data. When a complete cycle of discharging from a fully charged state to a depleted state and then recharging is detected, the ratio of the actual discharge capacity of that cycle to the battery's rated capacity is calculated. This ratio is the single-cycle assessment value of the health state for that cycle. To improve the reliability of the estimation results, a multi-cycle sliding window averaging strategy is adopted, continuously recording the single assessment of the most recent preset number of complete cycles. After removing obvious outliers, the arithmetic mean of the remaining valid single evaluation values ​​is calculated, and this arithmetic mean is determined as the current battery health status estimate. The internal resistance is calculated by analyzing the dynamic response of the battery terminal voltage to the step change in the loop current. When a significant step change in the loop current is detected, such as during a switch in charging / discharging mode or a large adjustment in power, the terminal voltage values ​​before and after the change in the loop current are recorded, and the ratio of the difference between the two terminal voltage values ​​to the change in the loop current is used as the estimated ohmic internal resistance. To obtain an accurate steady-state terminal voltage, a preset time must be waited after the loop current step change until the polarization process stabilizes. The system is determined to have entered a steady state when the terminal voltage change rate is monitored and it falls below a preset threshold.The estimation of polarization resistance is achieved by analyzing the voltage relaxation process of the battery pack after a step change in the loop current. After the loop current undergoes a step change and enters a steady state, the voltage relaxation curve over time is recorded. This curve is fitted into a mathematical model containing a steady-state voltage component and an exponentially decaying polarization voltage component. The steady-state amplitude of the polarization voltage component is extracted from the fitting result. This steady-state amplitude is divided by the absolute value of the step change in the loop current that caused the relaxation process to obtain the estimated polarization resistance. The estimated ohmic resistance is added to the estimated polarization resistance to obtain the internal resistance value of the battery pack.

[0060] The present invention is further configured such that the health assessment module includes:

[0061] The basic health component is calculated based on the ratio of the battery pack's current health status to its initial health status.

[0062] The internal resistance change trend factor is calculated based on the relative rate of change of the battery pack internal resistance and the average internal resistance within a preset historical window.

[0063] The temperature stress history factor is calculated based on the cumulative operating time of the battery pack within the preset high temperature range.

[0064] The health stress tolerance of each battery pack is obtained by multiplying the baseline health component, the internal resistance change trend factor, and the temperature stress history factor. The health stress tolerance is negatively correlated with both the internal resistance change trend factor and the temperature stress history factor. Specifically, the baseline health component reflects the degree to which the battery's current health level is maintained relative to its initial state. The initial health state measured when the battery pack was put into use is read from non-volatile memory. The estimated current health state is divided by the initial health state to calculate the baseline health component. A larger baseline health component indicates a better degree of battery health maintenance, while a lower baseline health component indicates a worse degree of battery health maintenance. The more severe the battery capacity degradation, the more significant the internal resistance trend factor. This factor quantifies the rate of deterioration of the battery pack's internal resistance. It extracts a sequence of internal resistance measurements within a recent preset historical window and calculates the average value of this sequence as a baseline internal resistance. Then, it calculates the absolute value of the relative rate of change between the current internal resistance measurement and this baseline internal resistance, defining this absolute value as the internal resistance trend factor. The temperature stress history factor characterizes the cumulative stress experienced by the battery pack under high-temperature conditions. It continuously monitors the battery pack temperature, and when the temperature exceeds a preset high-temperature threshold, it begins recording the duration of the high-temperature condition. It also calculates the cumulative time spent in a high-temperature state within the most recent preset statistical period and the time spent in that period. The ratio of the total duration of each period is used as the baseline stress value. This baseline stress value is then corrected based on a preset temperature weighting coefficient. The baseline stress value is multiplied by the corresponding temperature weighting coefficient to obtain the temperature stress history factor, where the temperature weighting coefficient increases with increasing temperature to characterize the cumulative damage effect of high-temperature stress. The healthy stress tolerance is obtained by comprehensively calculating the baseline healthy component, the internal resistance change trend factor, and the temperature stress history factor. The synthesis process uses the baseline healthy component as the benchmark value and applies an exponential decay function to both the internal resistance change trend factor and the temperature stress history factor. This exponential decay function increases the value of the input factor as its value increases. The smaller the output value, the more it penalizes the rapid growth trend of internal resistance and effectively characterizes the negative impact of high temperature history accumulation on battery health. The basic health component is multiplied by the output values ​​of the internal resistance change trend factor and the temperature stress history factor after being processed by the exponential decay function to obtain the health stress tolerance. This multiplication relationship ensures that the deterioration of the state of any factor will lead to a decrease in the health stress tolerance value. Preset weighting coefficients are used to adjust the sensitivity of the internal resistance change trend factor and the temperature stress history factor in the exponential decay function. The weighting coefficients are determined by fitting experimental data and can be adaptively adjusted for different battery chemical systems.

[0065] The present invention is further configured such that the boundary calculation module includes:

[0066] Based on the real-time state of charge and temperature of each battery pack, combined with the preset electrochemical characteristics of the battery, the instantaneous maximum allowable charging power boundary and the maximum allowable discharging power boundary of the battery pack are determined.

[0067] The instantaneous maximum allowable charging power boundary and the maximum allowable discharging power boundary together constitute the maximum safe charging and discharging power boundary of the battery pack. Specifically, the current state of charge (SOC) and temperature data of the battery pack are obtained and used as a joint query key to retrieve a preset power boundary database. This database is obtained through experimental testing and uses SOC and temperature as indexes to construct a two-dimensional lookup table, storing the maximum allowable charging and discharging power under different operating conditions. During the query, the nearest experimental calibration reference point is located in the two-dimensional table based on the current SOC and temperature, and its basic power boundary value is obtained. Since the actual SOC and temperature of the battery pack usually do not directly match the discrete test points in the database, a bilinear interpolation algorithm is used to calculate the accurate power value. Specifically, the interval where the current SOC is located is first located in the SOC dimension, and the power value corresponding to the upper and lower boundaries of the interval is linearly interpolated. Then, this process is repeated in the temperature dimension for a second linear interpolation. The results of the two interpolations are combined to obtain the corresponding basic electrochemical power boundary. To ensure safety margin, real-time thermal data is further considered. Constraints and state-of-charge (SOC) protection constraints are used to correct the aforementioned basic electrochemical power boundaries: The thermal constraint boundary is calculated based on the safety difference between the current temperature of the battery pack and the preset maximum allowable temperature, combined with the thermal capacity parameters of the battery pack, to determine the maximum allowable heat generation power to keep the temperature rise within a safe range. Subsequently, combined with the equivalent thermal resistance parameter characterizing the performance of the heat dissipation system, which reflects the overall heat dissipation efficiency from the heat source of the battery pack to the external ambient air, the maximum allowable heat generation power is divided by this total thermal resistance parameter to obtain the maximum allowable electrical power corresponding to ensuring thermal stability. This power value is the thermal constraint boundary. The SOC protection boundary is generated by querying a preset derating curve table when the battery pack's SOC approaches a preset limit. This table defines the mapping relationship between power reduction as the SOC deviates from the safe range. Finally, by comparing the basic electrochemical power boundary, the thermal constraint boundary, and the SOC protection boundary, the minimum value is selected as the final maximum safe charge and discharge power boundary of the battery pack, thereby ensuring that the power allocation command is always within strict safety limits.

[0068] The present invention is further configured such that the power distribution module includes:

[0069] A system power allocation optimization model is constructed, in which the power allocation value of each battery pack is set as the optimization variable, the total power demand of the system is set as the equality constraint, and the maximum safe charge and discharge power boundary and the state of charge safety window of each battery pack are set as the inequality constraint.

[0070] Define the objective function of the system power allocation optimization model. The objective function contains two terms: the first term is the sum of the aging cost terms of each battery pack, where the aging cost term of each battery pack is composed of the ratio of the square of its power allocation value to the square of its health stress tolerance; the second term is the largest single aging cost term among all battery packs.

[0071] A convex optimization algorithm is used to solve the system power allocation optimization model. By minimizing the objective function and obtaining the optimal solution that satisfies all constraints, the optimal power allocation command for each battery pack is generated. Specifically, the power allocation module generates the optimal power command by constructing and solving the system power allocation optimization model. Its core objective is to minimize the overall aging risk of the system while meeting the total system power demand. The construction of this system power allocation optimization model first sets the power allocation value of each battery pack as the optimization variable, which physically represents the actual charge and discharge power command to be issued. The system power allocation optimization model must follow three hard constraints: first, a power balance constraint, requiring the sum of the power allocation values ​​of all battery packs to be strictly equal to the total system demand to ensure energy balance; second, a safety boundary constraint, limiting the power allocation value of each battery pack to not exceed its maximum safe charge and discharge power boundary; and third, a state of charge window constraint, ensuring that the predicted state of charge of each battery pack remains within the preset safe range after power allocation. The objective function of the power allocation optimization model is constructed as the sum of two terms to quantify and minimize the system aging cost: the first term is the sum of the aging cost terms of each battery pack, where each individual aging cost term is the ratio of the square of the power allocation value to the square of the health stress tolerance. This design encourages high-tolerance battery packs to take on more power due to their lower cost terms, while protecting low-tolerance battery packs. The second term is the value of the largest single aging cost term among all battery packs, scaled by a weighting coefficient. Its purpose is to implement the principle of minimizing the maximum stress and prevent overloading of individual battery packs. The weighting coefficient is calibrated by combining previous battery cycle aging tests and system simulation methods to achieve the best balance between balancing the total system aging cost and avoiding single-pack overload. Since the power allocation optimization model of this system is a convex optimization problem, algorithms such as the interior point method are used for efficient solution. By iteratively searching for the set of power allocation values ​​that minimize the objective function under constraints, the optimal power allocation command for each battery pack is finally generated within the control cycle.

[0072] The present invention is further configured such that the power control module includes:

[0073] The optimal power allocation command is sent to the power converter corresponding to each battery pack.

[0074] Based on the optimal power allocation command, a pulse width modulation (PWM) signal is generated through a current closed-loop control algorithm, and this signal is used to drive the power converter to achieve precise control of the charging and discharging current of each battery pack. The invention is further configured such that the current closed-loop control algorithm calculates and generates the duty cycle of the PWM signal used to drive the power switching devices in the power converter based on the deviation between the optimal power allocation command and the actual output power of the battery pack. Specifically, the generated optimal power allocation command is sent to the power converter corresponding to each battery pack. After receiving the command, the power converter divides it by the real-time terminal voltage of the battery pack to convert it into the current command value to be tracked. This current command value serves as the setpoint for the current closed-loop control algorithm. The core of the current closed-loop control algorithm lies in achieving precise power tracking through real-time feedback and compensation. The specific process is as follows: continuously collecting data from each battery pack... The actual output current is compared with the current command value to calculate the real-time deviation value. This deviation value is sent to the current regulator, which calculates the control quantity according to the magnitude and trend of the deviation value and a preset control law. This control quantity is compared with a preset fixed-frequency triangular carrier wave to generate a corresponding pulse width modulation signal duty cycle. The generation logic is as follows: when the actual output current is lower than the current command value, the control algorithm will increase the duty cycle; when the actual output current is higher than the current command value, the duty cycle will decrease. Finally, the generated pulse width modulation signal with a specific duty cycle is directly applied to the control electrode of the fully controlled power switching device in the power converter. By changing its switching state, the charging and discharging current flowing through the battery pack is precisely adjusted, thereby achieving independent and precise control of the output power of each battery pack and completing the grid-connected transmission of photovoltaic energy.

[0075] The invention is further configured such that the system also includes a visualization module for real-time display of the internal state parameters, health stress tolerance, and optimal power allocation instructions of each battery pack. Specifically, the visualization module acquires the internal state parameters, health stress tolerance, and optimal power allocation instructions of each battery pack in real time, and performs timestamp alignment and formatting on the received data to ensure the consistency and real-time performance of the displayed information. This module adopts a multi-view collaborative interface layout to transform data into graphical elements for comprehensive display, specifically including: providing an independent visualization area for each battery pack through a single state view, combining digital and graphical components to display its internal state parameters in real time, and targeting health... Stress tolerance is visually differentiated using a technique that maps numerical values ​​to preset color ranges, such as green-yellow-red bands corresponding to safety, caution, and risk levels. A historical trend view provides a customizable time range for querying, displaying the historical trajectory of health stress tolerance in curve form, and supporting comparative analysis of trends across multiple battery packs. Bar charts or radar charts are used to centrally display the optimal power allocation instructions for each battery pack, and the relative height or numerical labels of graphical elements demonstrate the load balancing allocation strategy based on health status. Through the collaborative display of these views, operators are provided with a global understanding and decision support for the overall health status of the system and optimized operating strategies.

[0076] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A photovoltaic grid-connected high-efficiency energy storage and transmission system, characterized in that, include: Data acquisition module: used to synchronously acquire the terminal voltage, loop current and temperature of each battery pack based on the sensing devices corresponding to each battery pack; State estimation module: used to determine the internal state parameters of each battery pack based on terminal voltage, loop current and temperature, the internal state parameters including state of charge, state of health and internal resistance; Health assessment module: used to calculate the health stress tolerance of each battery pack based on its health status, historical trend of internal resistance, and historical temperature data; Boundary calculation module: used to determine the maximum safe charge and discharge power boundary of each battery pack based on the current state of charge and temperature of each battery pack; Power allocation module: used to generate optimal power allocation instructions for each battery pack based on health stress tolerance and maximum safe charge and discharge power boundaries; Power control module: Used to send the optimal power allocation command to the power converter corresponding to each battery pack, control each battery pack to perform charging and discharging operations according to the specified power, and realize the grid-connected transmission of photovoltaic energy.

2. The photovoltaic grid-connected high-efficiency energy storage and transmission system according to claim 1, characterized in that, The data acquisition module includes: A synchronization signal is generated in each control cycle to trigger the synchronous acquisition of terminal voltage, loop current and temperature of each battery pack. The acquired raw terminal voltage, loop current, and temperature data are digitally filtered to suppress high-frequency noise and remove outliers, and the pre-processed terminal voltage, loop current, and temperature are output.

3. The photovoltaic grid-connected high-efficiency energy storage and transmission system according to claim 1, characterized in that, The state estimation module includes: The state of charge of each battery pack is estimated by using the ampere-hour integral method and fusing the extended Kalman filter algorithm. By analyzing complete charge-discharge cycle data of the battery pack, the health status of each battery pack can be estimated. The internal resistance of each battery pack is calculated based on the dynamic response of the terminal voltage and loop current of the battery pack.

4. The photovoltaic grid-connected high-efficiency energy storage and transmission system according to claim 1, characterized in that, The health assessment module includes: The basic health component is calculated based on the ratio of the battery pack's current health status to its initial health status. The internal resistance change trend factor is calculated based on the relative rate of change of the battery pack internal resistance and the average internal resistance within a preset historical window. The temperature stress history factor is calculated based on the cumulative operating time of the battery pack within the preset high temperature range. The health stress tolerance of each battery pack is obtained by multiplying the basic health component, the internal resistance change trend factor, and the temperature stress history factor. The health stress tolerance is negatively correlated with the internal resistance change trend factor and the temperature stress history factor.

5. A photovoltaic grid-connected high-efficiency energy storage and transmission system according to claim 1, characterized in that, The boundary calculation module includes: Based on the real-time state of charge and temperature of each battery pack, combined with the preset electrochemical characteristics of the battery, the instantaneous maximum allowable charging power boundary and the maximum allowable discharging power boundary of the battery pack are determined. The instantaneous maximum allowable charging power boundary and the maximum allowable discharging power boundary together constitute the maximum safe charging and discharging power boundary of the battery pack.

6. The photovoltaic grid-connected high-efficiency energy storage and transmission system according to claim 1, characterized in that, The power distribution module includes: A system power allocation optimization model is constructed, in which the power allocation value of each battery pack is set as the optimization variable, the total power demand of the system is set as the equality constraint, and the maximum safe charge and discharge power boundary and the state of charge safety window of each battery pack are set as the inequality constraint. Define the objective function of the system power allocation optimization model. The objective function contains two terms: the first term is the sum of the aging cost terms of each battery pack, wherein the aging cost term of each battery pack is composed of the ratio of the square of its power allocation value to the square of its health stress tolerance; the second term is the largest single aging cost term among all battery packs. A convex optimization algorithm is used to solve the system power allocation optimization model. By minimizing the objective function and obtaining the optimal solution that satisfies all constraints, the optimal power allocation command for each battery pack is generated.

7. A photovoltaic grid-connected high-efficiency energy storage and transmission system according to claim 1, characterized in that, The power control module includes: The optimal power allocation command is sent to the power converter corresponding to each battery pack. Based on the optimal power allocation command, a pulse width modulation signal is generated through a current closed-loop control algorithm, and this signal is used to drive the power converter to achieve precise control of the charging and discharging current of each battery pack.

8. A photovoltaic grid-connected high-efficiency energy storage and transmission system according to claim 7, characterized in that, The current closed-loop control algorithm calculates and generates the duty cycle of the pulse width modulation signal used to drive the power switching devices in the power converter based on the deviation between the optimal power allocation command and the actual output power of the battery pack.

9. A photovoltaic grid-connected high-efficiency energy storage and transmission system according to claim 1, characterized in that, The system also includes a visualization module for displaying the internal state parameters, health stress tolerance, and optimal power allocation commands of each battery pack in real time.

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