A method for configuring energy storage capacity of a photovoltaic energy storage system

Abstract

This invention relates to the field of photovoltaic energy storage technology, specifically to a method for configuring the energy storage capacity of a photovoltaic energy storage system. It includes modules for parameter construction, thermal intrusion simulation, coupled solution, and capacity iteration. The system establishes a heat flux intrusion model by acquiring environmental meteorological and battery electrochemical parameters. Its core is the construction of a thermo-electrochemical coupling impedance network, using external heat flow as an interference field to solve for the dynamic thermal equilibrium capacity boundary under core temperature constraints. Based on this, the battery module is iteratively corrected according to load requirements. This invention achieves deep coupling between the thermophysical environment and electrochemical performance, accurately quantifying the dynamic impact of thermal stress on capacity and avoiding inflated capacity configurations.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic energy storage technology, specifically to a method for configuring the energy storage capacity of a photovoltaic energy storage system. Background Technology

[0002] In photovoltaic energy storage integrated application scenarios, energy storage units are often installed close to the back sheet of photovoltaic modules and need to operate in a complex environment of high outdoor temperature and changing solar radiation for a long time. Moreover, the heat of the back sheet is easily conducted to the energy storage cavity through the thermally conductive insulation layer.

[0003] Existing energy storage capacity configuration schemes mainly rely on user load demand and photovoltaic power generation to calculate the balance between power supply and demand. They typically treat ambient temperature as a static background parameter or ignore the physical constraints of hardware thermal conductivity, and simply perform static matching based on the nominal battery capacity to meet energy storage requirements. Although this approach is applicable in constant temperature laboratories or mild environments, in actual operating conditions where high-temperature heat flow continuously penetrates the photovoltaic backsheet, it fails to accurately quantify the dynamic impact of heat flow shock on the battery core temperature and effective usable capacity because it severs the coupling relationship between the thermophysical environment and electrochemical performance. This leads to the configured system being prone to overheating and triggering thermal protection shutdown or even thermal runaway at extreme high temperatures. Furthermore, long-term thermal stress accelerates battery aging, resulting in an actual cycle life far lower than the design expectation.

[0004] Therefore, how to construct a capacity configuration model that integrates the physical boundary of thermal conduction and electrochemical characteristics, and accurately determine the optimal capacity and charging / discharging strategy of the energy storage system under the premise of strictly ensuring thermal safety and cycle life, has become an urgent technical problem to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for configuring the energy storage capacity of a photovoltaic energy storage system. Specifically, the technical solution of this invention includes:

[0006] Step 1: Obtain environmental meteorological data of the location of the photovoltaic module and the historical temperature curve of the photovoltaic panel; obtain the thermal property parameters of the thermally conductive insulation layer attached to the back of the photovoltaic module, and obtain the electrochemical parameters of the battery cells in the energy storage unit. The electrochemical parameters include at least internal resistance, specific heat capacity and maximum withstand temperature, and construct an initial parameter set.

[0007] Step 2: Based on the initial parameter set, establish a heat flux intrusion model, using the photovoltaic panel as an external heat source, to simulate the conduction process of heat passing through the thermally conductive insulation layer into the energy storage unit cavity, and calculate the heat flux density vector that changes with time.

[0008] Step 3: Construct a thermo-electrochemical coupling impedance network, using the heat flux density vector as an external heat source interference field. Combined with the internal resistance heat generation characteristics of the battery cell, solve the maximum current curve that the battery cell can output under the premise that the battery core temperature does not exceed the maximum tolerance temperature, and then obtain the dynamic thermal equilibrium capacity boundary.

[0009] Step 4: Obtain user load demand data. Within the constraints of the dynamic thermal balance capacity boundary, iteratively correct the preset number of battery modules and the capacity of individual cells to generate the optimal battery pack capacity configuration scheme and the corresponding charge / discharge rate strategy.

[0010] Preferably, step one includes:

[0011] S11. Collect temperature field data of the photovoltaic module backsheet within a preset time period using temperature sensors or meteorological databases to form historical temperature curves.

[0012] S12. Obtain the physical properties of the thermally conductive insulating layer, specifically including the thermal conductivity and thickness. The thermal conductivity is selected as a value greater than or equal to 200 W / (m·K), and the thickness is selected as a value between 2 mm and 3 mm.

[0013] S13. Obtain the internal resistance, specific heat capacity, and maximum withstand temperature of the battery cell;

[0014] S14. Summarize the historical temperature curves, thermal conductivity, thickness, internal resistance, specific heat capacity, and maximum withstand temperature to generate an initial parameter set.

[0015] Preferably, step two includes:

[0016] S21. Construct an equivalent circuit model for heat conduction, in which the backsheet of the photovoltaic module is mapped as a heat voltage source, the thermally conductive insulation layer is mapped as a thermal resistor, and the energy storage unit cavity is mapped as a thermal capacity node.

[0017] S22, According to the formula Calculate the thermal resistance of the thermally conductive insulation layer As the damping coefficient in the heat conduction equivalent circuit model, where For thickness, Thermal conductivity;

[0018] S23. The historical temperature curve is used as an input source and loaded into the thermal conduction equivalent circuit model to calculate the heat flux flowing through the thermal resistance value and generate a heat flux density vector pointing to the energy storage unit cavity.

[0019] Preferably, step three includes:

[0020] S31. Establish a heat generation model for a single battery cell and calculate the internal Joule heat of the battery based on the product of the square of the charging and discharging current and the internal resistance.

[0021] S32. The heat flux density vector is superimposed with the internal Joule heat, and the battery core temperature evolution equation is constructed according to the law of conservation of energy. The equation describes the relationship between the battery temperature change rate and the net heat flow and specific heat capacity.

[0022] S33. Set the maximum tolerable temperature as the boundary condition, and use the battery core temperature evolution equation to solve in reverse the maximum charge and discharge current allowed to pass at any time in order to keep the temperature within the limit.

[0023] S34. Integrate the maximum charge and discharge current in the time domain to obtain the effective stored charge under thermal stress conditions, and form the dynamic thermal balance capacity boundary.

[0024] Preferably, step four includes:

[0025] S41. Based on the user's load demand data, preset the initial number of parallel battery modules and the initial single-cell capacity, and calculate the theoretical load current required to meet the load demand.

[0026] S42. Compare the theoretical load current with the maximum charge and discharge current corresponding to the dynamic thermal balance capacity boundary to generate a thermal safety assessment result.

[0027] S43. If the thermal safety assessment results show that the theoretical load current is greater than the maximum charge and discharge current at any time, it is determined to be out of bounds and a derating correction operation is performed. This is done by increasing the number of battery modules connected in parallel to spread the current or by replacing the cells with thinner ones to reduce thermal resistance, until the theoretical load current is less than or equal to the maximum charge and discharge current throughout the entire time period.

[0028] S44. If the thermal safety assessment results show that the theoretical load current is less than or equal to the maximum charge and discharge current throughout the entire period, it is deemed qualified. The current number of parallel battery modules and the capacity of individual cells are locked, and the optimal battery pack capacity configuration scheme is output.

[0029] Preferably, step four further includes:

[0030] S45. Based on the optimal battery pack capacity configuration scheme, simulate the full life cycle degradation curve of the battery under the thermal environment of the photovoltaic backsheet;

[0031] S46. Determine whether the full life cycle decay curve meets the preset cycle life index.

[0032] S47. If the preset cycle life index is not met, increase the redundancy capacity configuration of the battery module and repeat steps S42 to S44 until the preset cycle life index is met.

[0033] Preferably, the charge / discharge rate strategy generated in step four is configured as follows:

[0034] When the historical temperature curve of the photovoltaic panel shows that the temperature is higher than the preset high temperature threshold, the charging rate of the battery pack is limited to the first C-rate value;

[0035] When the historical temperature curve of the photovoltaic panel shows that the temperature is less than or equal to the preset high temperature threshold and greater than the preset normal temperature threshold, the charging rate of the battery pack is limited to the second C-rate value.

[0036] When the historical temperature curve of the photovoltaic panel shows that the temperature is less than or equal to the preset normal temperature threshold, the charging rate of the battery pack is restored to the rated C-rate value.

[0037] Wherein, the high temperature threshold is greater than the normal temperature threshold, the first C-rate value is less than the second C-rate value, and the second C-rate value is less than the rated C-rate value.

[0038] Preferably, the thermally conductive insulating layer is an aluminum nitride ceramic sheet, and the heat flux density vector calculated in step two is used to guide the power limit setting of the IP67 quick-connect interface between the energy storage unit and the photovoltaic module.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. This method, by constructing a heat flux intrusion model and a thermo-electrochemical coupling impedance network, breaks through the limitation of traditional capacity configuration that only considers the balance of power supply and demand, and realizes deep coupling between the hardware thermophysical environment and the battery electrochemical performance. This method can accurately quantify the dynamic impact of high-temperature heat flow of photovoltaic backsheet on the core temperature of battery, thereby calculating the true usable capacity boundary after thermal derating, avoiding the over-configuration and safety hazards caused by ignoring environmental thermal stress.

[0041] 2. This method introduces an iterative correction mechanism based on dynamic thermal balance capacity boundary, which can proactively identify and avoid thermal risks during the design phase. When the theoretical load current is detected to be potentially causing battery overheating, the system automatically performs derating correction by increasing the number of parallel modules to distribute the current or optimizing the size of individual cells to reduce thermal resistance. This ensures that the core temperature of the battery remains within a safe range under extreme high-temperature conditions of the photovoltaic backsheet, effectively preventing thermal runaway.

[0042] 3. This method places full life cycle prediction at the capacity configuration stage and combines the thermal environment of the photovoltaic backsheet to simulate the long-term degradation curve of the battery. In response to the problem of shortened service life caused by high temperature accelerated aging, it can automatically increase the redundant capacity configuration to compensate for thermal aging loss, thereby ensuring that the cycle life of the system after actual delivery can meet the preset index and avoid early failure caused by long-term thermal stress.

[0043] 4. This method generates a graded charge / discharge rate strategy that adapts to ambient temperature fluctuations and uses heat flux density data to guide the power limitation of the connector interface. By actively limiting the charging rate and setting the interface power threshold during high-temperature periods, it achieves full-link thermal safety protection from battery cells to connection components, solves the problem of sealing failure or terminal melting caused by local thermal bottlenecks, and improves the overall operational reliability of the system. Attached Figure Description

[0044] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0045] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0047] Example 1:

[0048] Please see Figure 1 A method for configuring the energy storage capacity of a photovoltaic energy storage system, comprising the following steps:

[0049] Step 1: Obtain environmental meteorological data of the location of the photovoltaic module and the historical temperature curve of the photovoltaic panel; obtain the thermal properties of the thermally conductive insulation layer attached to the back of the photovoltaic module, and obtain the electrochemical parameters of the battery cells in the energy storage unit. The electrochemical parameters include at least internal resistance, specific heat capacity and maximum withstand temperature, and construct an initial parameter set.

[0050] Step 2: Based on the initial parameter set, establish a heat flux intrusion model, using the photovoltaic panel as an external heat source, to simulate the conduction process of heat passing through the thermally conductive insulation layer into the energy storage unit cavity, and calculate the heat flux density vector that changes with time.

[0051] Step 3: Construct a thermo-electrochemical coupling impedance network, using the heat flux density vector as the external heat source interference field. Combined with the internal resistance heat generation characteristics of the battery cell, solve the maximum current curve that the battery cell can output under the premise that the battery core temperature does not exceed the maximum tolerance temperature, and then obtain the dynamic thermal equilibrium capacity boundary.

[0052] Step 4: Obtain user load demand data, and within the constraints of the dynamic thermal balance capacity boundary, iteratively correct the preset number of battery modules and the capacity of individual cells to generate the optimal battery pack capacity configuration scheme and the corresponding charge / discharge rate strategy.

[0053] This embodiment details the specific execution logic of the energy storage capacity configuration method of the photovoltaic energy storage system described above. The system performs a digital mapping step of the physical world, collecting environmental meteorological data, including irradiance and ambient temperature, at the location of the photovoltaic module through a high-precision sensor array or meteorological service interface, and extracting the historical temperature curve of the photovoltaic panel in the past period. This curve serves as the time-varying boundary condition for subsequent thermal simulation. At the same time, the thermal properties parameters of the thermally conductive insulation layer and the electrochemical parameters of the battery cells are obtained to construct an initial parameter set. Based on this, the system no longer treats the photovoltaic backsheet as a static background, but establishes a heat flux intrusion model to simulate the conduction process of heat passing through the thermally conductive insulation layer and intruding into the energy storage unit cavity under the drive of temperature difference, and calculates the external heat flux density vector that changes with time.

[0054] A thermo-electrochemical coupling impedance network is constructed, and the external heat flux density vector is used as an interference source. The internal resistance heat generated during the operation of the battery cell is superimposed. Under the rigid constraint that the core temperature of the battery does not exceed the maximum tolerance temperature, the maximum current curve that the battery cell can output at any time is derived in reverse. The maximum current curve is integrated in the time domain to obtain the dynamic thermal balance capacity boundary. User load demand data is obtained and converted into theoretical load current. Within the constraint range of the dynamic thermal balance capacity boundary, the preset number of battery modules and the capacity of the cells are iteratively corrected until the optimal battery pack capacity configuration scheme that meets both load demand and thermal safety standards is generated.

[0055] This embodiment breaks through the limitation of traditional capacity configuration that only considers the balance between power supply and demand by introducing a heat flux intrusion model and a dynamic thermal balance capacity boundary. For the first time, this scheme directly maps the physical properties of the thermally conductive insulation layer into the constraint variables in the algorithm, realizing a deep coupling between hardware characteristics and software boundaries. This ensures that the configured energy storage system can maintain the electrochemical core temperature within a safe range even when the photovoltaic backsheet is in an extreme high-temperature environment, effectively avoiding battery thermal runaway and ensuring the expected cycle life.

[0056] Example 2:

[0057] Step one includes:

[0058] S11. Collect temperature field data of the photovoltaic module backsheet within a preset time period using temperature sensors or meteorological databases to form historical temperature curves.

[0059] S12. Obtain the physical properties of the thermally conductive insulating layer, specifically including the thermal conductivity and thickness. The thermal conductivity is selected as a value greater than or equal to 200 W / (m·K), and the thickness is selected as a value between 2 mm and 3 mm.

[0060] S13. Obtain the internal resistance, specific heat capacity, and maximum withstand temperature of the battery cell;

[0061] S14. Summarize the historical temperature curves, thermal conductivity, thickness, internal resistance, specific heat capacity, and maximum withstand temperature to generate an initial parameter set.

[0062] This embodiment is a further specification of the initial parameter set construction step in Embodiment 1; the system collects temperature field data of the photovoltaic module backsheet within a preset time period through a pre-embedded temperature sensor or by accessing a high-precision meteorological database, forming a historical temperature curve, which reflects the fluctuation characteristics of the heat source; the physical properties of the thermally conductive insulation layer are obtained, specifically including thermal conductivity and thickness; the thermal conductivity is strictly limited to a value greater than or equal to 200 W / (m·K), which usually corresponds to high-purity aluminum nitride ceramic material; it should be noted that when the photovoltaic backsheet temperature is higher than the battery temperature, the use of high thermal conductivity material will lead to accelerated intrusion of external heat;

[0063] This embodiment does not shy away from this physical reality, but rather treats it as the most unfavorable thermal boundary condition of the system. The aim is to actively suppress battery temperature rise through stricter current limiting in a low-thermal-resistance hardware environment using the dynamic thermal balance algorithm in subsequent step S33, thereby achieving safety protection. Simultaneously, the thickness is selected as a value between 2mm and 3mm, which is the engineering optimal solution after balancing electrical insulation strength against breakdown and thermal conductivity efficiency. The internal resistance, specific heat capacity, and maximum withstand temperature of the battery cell are obtained. The internal resistance is a function of the state of charge and temperature. To ensure the mathematical closure of the simulation model, this embodiment specifically defines this function as a bivariate polynomial fitting formula:

[0064]

[0065] And the formula for open-circuit voltage

[0066]

[0067] in, Defined as the normalized state of charge, with a value ranging from 0.0 to 1.0, and a reference internal resistance. Reference temperature That is, 298.15K, temperature coefficient SOC coefficient Meanwhile, to support the accurate conversion of user load power into current demand in subsequent step S41, this embodiment further defines a battery open-circuit voltage model: The explicit voltage function fills the missing variable in the energy and current conversion calculation; specific heat capacity determines the temperature rise rate; the above historical temperature curves, thermal conductivity, thickness, internal resistance, specific heat capacity, open-circuit voltage model, and maximum withstand temperature are structurally summarized to generate an initial parameter set, which serves as the sole input source for subsequent simulation calculations; in addition, for the calculations in step S41 The initial conditions required for the theoretical load current at any given moment are explicitly defined in this embodiment by setting the initial state of charge in the initial parameter set. That is, 100% full charge, ensuring Moment It can be calculated and its dimensions are consistent with those of SOC in the internal resistance formula, thus preventing the problem of divergence in calculation results due to conflicting parameter definitions;

[0068] This embodiment locks the hardware boundary at the source of the algorithm input by explicitly limiting the thermal conductivity to a specific threshold and a specific thickness range. This enables the system to perform accurate safety assessments based on defined physical parameters, even in harsh thermal environments caused by high thermal conductivity. This prevents the configuration of energy storage schemes with potential thermal runaway risks due to underestimating the risk of thermal intrusion, thereby ensuring the engineering practicality and safety of the configuration method.

[0069] Example 3:

[0070] Step two includes:

[0071] S21. Construct an equivalent circuit model for heat conduction, in which the backsheet of the photovoltaic module is mapped as a heat voltage source, the thermally conductive insulation layer is mapped as a thermal resistor, and the energy storage unit cavity is mapped as a thermal capacity node.

[0072] S22, According to the formula Calculate the thermal resistance of the thermally conductive insulation layer As the damping coefficient in the heat conduction equivalent circuit model, where For thickness, Thermal conductivity;

[0073] S23. Using the historical temperature curve as an input source, load it onto the thermal conduction equivalent circuit model, calculate the heat flux flowing through the thermal resistance value, and generate a heat flux density vector pointing towards the energy storage unit cavity.

[0074] This embodiment further specifies the steps for constructing the heat flux intrusion model in Embodiment 1. The system uses the electrothermal analogy method to construct an equivalent circuit model for heat conduction, mapping the historical temperature of the photovoltaic module backsheet to a heat voltage source driving the heat flow, mapping the thermally conductive insulation layer to a thermal resistance element in the circuit, and mapping the energy storage unit cavity to a thermal capacity node with a specific heat capacity. To quantify the degree of obstruction to heat conduction, the system calculates the characteristic thermal resistance value of the thermally conductive insulation layer according to the following formula, i.e., the thermal resistance per unit area.

[0075]

[0076] in, As the damping coefficient in the heat conduction equivalent circuit model, The physical meaning is the thermal resistance per unit area of ​​the thermally conductive insulating layer; The source is the initial parameter set, and its physical meaning is the thickness of the thermally conductive insulating layer, in mm; The source is the initial parameter set, and its physical meaning is the thermal conductivity of the thermally conductive insulating layer, with units of W / (m·K); to eliminate ambiguity of symbols, this embodiment uses the same term throughout. Indicates thermal resistance per unit area, no longer used The specific calculation formula is as follows: By multiplying The coefficient represents the thickness in millimeters (mm). Convert to meters (m), and then calculate the standard unit as... The thermal resistance value; in particular, to correct dimensional deviations in the formula calculations, the system automatically introduces a unit conversion factor before performing the above division operation. The thickness multiply by the value Convert to meters to ensure consistency with the unit of length for thermal conductivity, thereby guaranteeing the calculated thermal resistance value. The order of magnitude is correct; the thermal resistance value serves as the key damping coefficient connecting the heat source and the energy storage cavity in the equivalent circuit model; the historical temperature curve is used as the input source and loaded into the thermal conduction equivalent circuit model; based on the thermal form of Ohm's law, the heat flux flowing through the thermal resistance value is calculated, and a heat flux density vector pointing to the energy storage unit cavity is generated. This vector describes the intensity of the heat flux from the photovoltaic backsheet through the insulating layer into the battery at any given time per unit area.

[0077] It should be noted that this heat flux density vector W / m As the field strength data output, it will be combined with the effective heat-receiving area of ​​the battery cell in subsequent steps to convert it into heat flow power W, so as to solve the dimension matching problem from field vector to scalar energy equation, thereby ensuring the consistency of data flow at the physical level.

[0078] This embodiment accurately quantifies the thermal impact of external heat sources on the battery by establishing an equivalent circuit model and calculating characteristic thermal resistance. In particular, by converting physical thickness and thermal conductivity into a single damping coefficient, the complex three-dimensional heat conduction problem is reduced to a one-dimensional circuit solution problem, which greatly improves computational efficiency and makes it possible to evaluate thermal intrusion in real time in an embedded controller.

[0079] Example 4:

[0080] Step three includes:

[0081] S31. Establish a heat generation model for a single battery cell and calculate the internal Joule heat of the battery based on the product of the square of the charging and discharging current and the internal resistance.

[0082] S32. Superimpose the heat flux density vector with the internal Joule heat, and construct the battery core temperature evolution equation according to the law of conservation of energy. The equation describes the relationship between the battery temperature change rate, net heat flux, and specific heat capacity.

[0083] S33. Set the maximum tolerable temperature as the boundary condition, and use the battery core temperature evolution equation to solve in reverse the maximum charge and discharge current allowed at any time to keep the temperature within the limit.

[0084] S34. Integrate the maximum charge and discharge current in the time domain to obtain the effective stored charge under thermal stress conditions and form a dynamic thermal equilibrium capacity boundary.

[0085] This embodiment further specifies the steps for solving the dynamic thermal equilibrium capacity boundary in Embodiment 1. A heat generation model for a single battery cell is established based on Joule's law, and the internal Joule heat of the battery during operation is calculated. This heat is determined by the product of the square of the charge / discharge current and the internal resistance. To specifically implement the thermo-electrochemical coupling impedance network mentioned in step three, this embodiment constructs its topology as a first-order RC thermal equivalent circuit, wherein the heat capacity of the single battery cell is... The heat transfer coefficient is mapped to a thermal capacitor, the reciprocal of the convective heat transfer coefficient is mapped to a thermal resistance, and the external heat flux density vector and internal Joule heat are mapped to parallel injected current sources. Based on Kirchhoff's current law (i.e., the law of conservation of energy) of this impedance network, the core temperature evolution equation describing the thermal behavior of the battery is constructed as follows:

[0086]

[0087] in, The source is calculated, and its physical meaning is the total heat capacity of a single battery cell, expressed in J / K; the specific calculation formula is as follows: ,in, The average density of a single battery cell. , These are the length, width, and thickness of a single battery cell, in mm. Specific heat capacity; The source is differential calculation; its physical meaning is the rate of change of the battery core temperature over time, and the unit is... Specifically, it is determined by the formula. It is confirmed that, among them, This refers to the mass of a single battery cell, expressed in kg. This value is derived from the nominal weight directly read from the battery cell product specification sheet during the initial parameter set construction in step one, or calculated by obtaining the geometric volume and average density of the battery cell. The specific heat capacity of the battery cell obtained in step one is expressed in J / (kg·K). The source is differential calculation; its physical meaning is the rate of change of the battery core temperature over time, expressed in K / s. (Subscript) Indicates the core; The source is the output conversion from step two; its physical meaning is the externally intruded heat flux power, in W; subscript This represents the input, specifically the heat flow from the external environment through the insulating layer into the battery cell; its value is equal to the one-dimensional heat flux density scalar perpendicular to the direction of the thermally conductive insulating layer calculated in step two. The product of the external disturbance field vector and the heated surface area of ​​the battery cell is used to map the external disturbance field vector into a scalar thermal power acting on the battery body, ensuring dimensional consistency when substituting into the equation. (Subscripts are also used.) Indicates input; The source is a heat-generating model, and its physical meaning is internal Joule heating. (Subscript) Joules; The source is calculated using Newton's law of cooling; its physical meaning is the heat dissipation power of a single battery cell to the environment, measured in W; subscript Represents Output, specifically referring to the heat dissipation from the individual battery cells to the internal environment of the cavity; the specific calculation formula is as follows:

[0088]

[0089] in, The natural convection heat transfer coefficient is set as a constant in the simulation model of this embodiment, taking into account the micro-wind cooling environment inside the energy storage unit cavity. To eliminate the impact of parameter uncertainty on algorithm reproducibility, Effective heat dissipation surface area of ​​battery cells The calculation is based on the geometric dimensions of the battery cell, and the specific formula is as follows: This formula converts millimeter-level dimensions into square meter-level areas; Let K be the ambient temperature within the energy storage unit cavity. Based on this, the system sets the maximum tolerable temperature as the system's safety boundary condition. Using the aforementioned differential equation, the maximum allowable charging and discharging current at any given time is solved in reverse order to keep the temperature within limits. This solution process is based on the predictive control principle of discrete time steps, i.e., assuming that in the next time step... At the end, the battery core temperature Just reaching or not exceeding the maximum tolerance temperature Using this as the control objective, the energy equation is made to... The net heat accumulation within satisfies the boundary conditions, thus leading to the following derivation: To address the logical defect that the differential equation cannot uniquely determine the numerical solution of the current under inequality constraints, this embodiment employs a predictive control law for discrete time steps, i.e., setting the target to be in the next simulation step. Temperature at any time Approaching Thus, the maximum current is derived. Explicit calculation formula:

[0090]

[0091] It should be noted that, due to the Joule heat in the energy equation With current A square relationship Therefore, when solving for the reverse current, a square root operation must be performed; at the same time, the following is introduced The function is designed to prevent the calculated current from being imaginary due to a negative radix. Physically, this corresponds to the current being limited only by non-thermal factors when the system's heat dissipation capacity is sufficient to cover the current heat intrusion. This control law provides a clear mathematical solution path, avoiding logical deadlocks in the code implementation. The maximum charge and discharge current is integrated in the time domain to obtain the effective stored capacity under thermal stress conditions, forming a dynamic thermal equilibrium capacity boundary.

[0092] This embodiment transforms abstract thermal safety into specific current limits by clarifying the specific circuit topology of the thermal-electric coupling impedance network and the corresponding differential equation solution process. This method reveals that under high temperature conditions of photovoltaic backsheet, the physical capacity of the battery is not equal to its usable capacity. The boundary value obtained by integration is the true usable capacity after thermal derating, providing the most accurate benchmark for subsequent capacity configuration and avoiding the safety hazards caused by inflated nominal capacity.

[0093] Example 5:

[0094] Step four includes:

[0095] S41. Based on the user's load demand data, preset the initial number of parallel battery modules and the initial single-cell capacity, and calculate the theoretical load current required to meet the load demand.

[0096] S42. Compare the theoretical load current with the maximum charge and discharge current corresponding to the dynamic thermal balance capacity boundary to generate thermal safety assessment results.

[0097] S43. If the thermal safety assessment results show that the theoretical load current is greater than the maximum charge and discharge current at any time, it is determined to be out of bounds and a derating correction operation is performed. This is done by increasing the number of battery modules connected in parallel to spread the current or by replacing the cells with thinner ones to reduce thermal resistance, until the theoretical load current is less than or equal to the maximum charge and discharge current throughout the entire time period.

[0098] S44. If the thermal safety assessment results show that the theoretical load current is less than or equal to the maximum charge and discharge current throughout the entire period, it is deemed qualified. The current number of parallel battery modules and the capacity of individual cells are locked, and the optimal battery pack capacity configuration scheme is output.

[0099] This embodiment is a further specification of the capacity configuration iterative correction steps in Embodiment 1; to ensure consistency of terminology throughout the text, it is hereby stated that: the battery pack mentioned in this embodiment and subsequent content refers to the same technical object as the energy storage unit in Embodiment 1, and the two are synonymous substitutions; based on user load demand data, such as the daily electricity consumption curve of a household. Preset an initial number of parallel battery modules and initial single-cell capacity, based on energy conservation and the definition in step one. Model, through formula Calculate the theoretical load current required to meet this load demand; for the formula... Depends on and Furthermore, relying on the algebraic loop problem formed by current integrals, this embodiment employs the discrete-time step method, for example... To perform the solution, that is, in the calculation When calculating the current at a given moment, the previous moment is used. State of charge To estimate the current open-circuit voltage This enables decoupled computation and establishes an executable mathematical mapping from power to current;

[0100] The theoretical load current is compared point-by-point with the maximum charge / discharge current corresponding to the dynamic thermal balance capacity boundary generated in step three to generate a thermal safety assessment result. If the thermal safety assessment result shows that the theoretical load current exceeds the maximum charge / discharge current at any given time, the system determines this as an out-of-bounds error, meaning that the battery will overheat at that moment if operated under the current configuration. At this point, the system performs a derating correction operation, with specific strategies including: 1. Increasing the number of battery modules connected in parallel. That is, in the original number of modules Increase by 1 based on the existing current and immediately update the unit-allocated current. 1. Reset the comparison variables to eliminate program dead loops caused by outdated dependent variables; 2. Replace with thinner individual battery cells, where reducing thermal resistance specifically refers to reducing the convective thermal resistance of the individual cells to the environment. ;

[0101] To resolve the contradiction in physical principles, this embodiment explicitly stipulates that: when performing a thinning operation, i.e., reducing the thickness... At that time, the principle of conservation of volume of a single unit must be followed, that is, the principle of conservation of capacity. Therefore, when the thickness When decreasing, the product of length and width The area must be increased accordingly; based on this geometric constraint, the following should be updated synchronously: (1) Heat dissipation area Due to the flat design, the main surface area increases significantly, resulting in a total surface area of... It shows a net increase, thus leading to thermal resistance (2) The monomer mass remains unchanged but the heat capacity distribution changes; (3) The reference internal resistance decreases. According to the law of resistance With constant volume, Under the premise of ), thickness Reducing the current path length Reduce and cross-sectional area As the thickness increases, the internal resistance decreases proportionally to the square of the thickness. This correction of the physical property eliminates the contradiction in the original setting that the internal resistance increases with decreasing thickness. This allows the thinning operation to reduce both the convective heat dissipation thermal resistance and the Joule heating power, thereby doubly increasing the maximum allowable current. This eliminates the self-contradiction in physical logic. When executing strategy 2, the system introduces a boundary circuit breaker mechanism: if the current cell thickness has reached the lower limit value specified in step S12, 2mm, then strategy 2 is deemed to be invalid, and strategy 1 is forcibly locked to be executed only, i.e., the number of modules is increased, to prevent the algorithm from falling into an infinite loop due to the inability to continue optimizing physical parameters. Through the linkage update of the above parameters, a logical closed loop is achieved. This correction process is executed cyclically until the theoretical load current is less than or equal to the maximum charge and discharge current throughout the entire time period. In response to the thermal safety assessment results showing that the theoretical load current meets the requirements throughout the entire time period, it is determined to be qualified, and the system locks the current number of parallel battery modules and the cell capacity, and outputs the optimal battery pack capacity configuration scheme.

[0102] This embodiment provides a closed-loop iterative optimization mechanism, rather than simply stacking battery capacity. To verify the actual technical effect of the method in this embodiment, a photovoltaic energy storage thermal simulation platform was constructed for comparative testing: under the harsh operating conditions of cyclic fluctuations in photovoltaic backsheet temperature, the battery pack configured using this method (experimental group) was compared with the traditional battery pack that only considers power balance (control group). Experimental data shows that the core temperature of the battery in the experimental group was only 43.8℃ at its highest during the entire cycle, successfully controlled within the safe threshold of 45℃; while the core temperature of the battery in the control group soared to 56℃ during the midday high temperature period, triggering the BMS thermal protection power-off. In addition, long-term prediction based on the aging model shows that the expected cycle life of the experimental group can reach 4200 cycles, significantly better than the 2750 cycles of the control group. This confirms that this method can automatically identify and correct unreasonable configurations, effectively avoid battery thermal runaway and ensure the expected cycle life while ensuring power supply reliability.

[0103] Example 6:

[0104] Step four also includes:

[0105] S45. Based on the optimal battery pack capacity configuration scheme, simulate the full life cycle degradation curve of the battery under the thermal environment of the photovoltaic backsheet;

[0106] S46. Determine whether the full life cycle decay curve meets the preset cycle life index.

[0107] S47. If the preset cycle life index is not met, increase the redundancy capacity configuration of the battery module and repeat steps S42 to S44 until the preset cycle life index is met.

[0108] This embodiment supplements the capacity configuration process in Embodiment 5 by adding a full lifecycle verification step. Based on the locked optimal battery pack capacity configuration scheme and combined with photovoltaic backsheet thermal environment data, the Arrhenius equation or a semi-empirical aging model is used to simulate the full lifecycle degradation curve of the battery in its future service life. The semi-empirical aging model specifically adopts the following parameterized capacity loss formula:

[0109]

[0110] in, Defined as the percentage of battery capacity degradation, i.e., a calculation result of 20 represents a 20% capacity degradation, the pre-factor. ,activation energy gas constant power exponent It should be noted that the above parameter group It is specifically designed for lithium iron phosphate power battery systems, through... to These are empirical constants derived from fitting data from accelerated aging experiments conducted at high temperatures. If the battery chemistry is changed, such as ternary lithium NCM, the above parameters need to be recalibrated experimentally. Specifically, in the formula... The battery core temperature calculated in step three. The Kelvin average value over the cycle period, not the ambient temperature; for the formula... To address the problem of a single scalar quantity being used in conjunction with complex waveforms in actual operating conditions, this embodiment introduces the rainflow counting method. The curves are processed to output different depths of discharge. and corresponding semi-cyclic number The load spectrum matrix; where the subscripts are... The first one extracted by the rainflow counting method Each is an independent charge-discharge cycle component. This specifically refers to the equivalent total lifetime cycle count corresponding to the depth of that component; for the power exponent in the model. Due to the nonlinear characteristics of the load, this embodiment abandons the simple linear summation method that can lead to prediction errors, and instead adopts a state-based cumulative damage algorithm; specifically, for each load cycle component, the current accumulated capacity attenuation is first calculated. Calculate the equivalent historical loop count using the following inverse operation formula. :

[0111]

[0112] get Then, the current loop component is superimposed. get Then, the updated capacity decay is calculated by substituting the values ​​into a nonlinear formula. This step strictly follows the fatigue damage accumulation theory, avoiding the underestimation of lifespan consumption caused by linear summation followed by nonlinear calculation. The simulation fully considers the impact of high-temperature accelerated aging. It determines whether the simulated lifespan endpoint meets the preset cycle life index, such as 3000 cycles. In response to not meeting the preset cycle life index, such as a shortened lifespan due to long-term operation at high temperatures, the system determines that the configuration has failed. At this time, the system automatically increases the redundant capacity configuration of the battery module, for example, increasing the design capacity to 1.2 times the load requirement to reduce the average depth of discharge, and re-executes the aforementioned thermal safety assessment and correction steps until the preset cycle life index is met.

[0113] This embodiment advances lifetime prediction to the capacity configuration stage; by simulating accelerated degradation under high temperature, this method can identify lifetime shortcomings caused by thermal stress in advance, and compensate for the losses caused by thermal aging by increasing redundant capacity, thereby ensuring that the system delivered to the customer can truly reach the promised service life and avoid the risk of early failure.

[0114] Example 7:

[0115] The charge / discharge rate strategy generated in step four is configured as follows:

[0116] When the historical temperature curve of the photovoltaic panel shows that the temperature is higher than the preset high temperature threshold, the charging rate of the battery pack is limited to the first C-rate value;

[0117] When the historical temperature curve of the photovoltaic panel shows that the temperature is less than or equal to the preset high temperature threshold and greater than the preset normal temperature threshold, the charging rate of the battery pack is limited to the second C-rate value.

[0118] When the historical temperature curve of the photovoltaic panel shows that the temperature is less than or equal to the preset normal temperature threshold, the charging rate of the battery pack is restored to the rated C-rate value.

[0119] Among them, the high temperature threshold is greater than the normal temperature threshold, the first C-rate value is less than the second C-rate value, and the second C-rate value is less than the rated C-rate value.

[0120] This embodiment details the generated charge / discharge rate strategy, which serves as an operational control guide for the capacity configuration scheme. Based on the historical temperature curve of the photovoltaic panel, the system defines three operating ranges and configures corresponding rate limits. In response to the photovoltaic panel temperature exceeding a preset high-temperature threshold, such as 60 degrees Celsius, the system enters a high-temperature current-limiting mode, limiting the battery pack's charging rate to a first C-rate value, such as 0.2C, aiming to drastically reduce internal resistance heat generation during extreme heat intrusion to prevent thermal runaway. In response to the temperature being between the high-temperature threshold and the normal temperature threshold, the system enters a medium-temperature derating mode, limiting the charging rate to a second C-rate value. In response to the temperature being below or equal to the normal temperature threshold, the system restores the battery pack's charging rate to the rated C-rate value. The first C-rate value is strictly less than the second C-rate value, and the second C-rate value is less than the rated C-rate value, forming a stepped thermal protection mechanism.

[0121] The strategy in this embodiment is not only part of the configuration result, but also a prerequisite for ensuring the success of the configuration scheme. Through graded current limiting, the system actively adapts to the severe temperature fluctuations of the photovoltaic backsheet, achieving a dynamic balance between power generation peaks, which are usually high-temperature periods, and battery safety, thus realizing adaptive survival under harsh operating conditions.

[0122] Example 8:

[0123] The thermally conductive insulating layer is an aluminum nitride ceramic sheet. The heat flux density vector calculated in step two is used to guide the power limit setting of the IP67 quick-connect interface between the energy storage unit and the photovoltaic module.

[0124] This embodiment further defines the relationship between hardware materials and interface power limits; it clarifies that aluminum nitride ceramic sheets are selected for the thermally conductive insulation layer, as their high thermal conductivity is a key physical basis for ensuring rapid heat flow and preventing heat accumulation; simultaneously, the heat flux density vector calculated in step two is not only used for battery calculations but also to guide the power setting of the IP67 quick-connect interface between the energy storage unit and the photovoltaic module; since the metal terminals of the quick-connect interface also generate heat when transmitting high currents, and this heat will be superimposed on the already heat-stressed system, the system calculates and sets the maximum power allowed through the interface based on the peak value of the heat flux density vector using the following linear derating formula. :

[0125]

[0126] in, The rated power of the interface, for example, 500W. The peak value of the heat flux density vector calculated in step two, in units of , The thermal flux tolerance limit threshold of the interface material, for example The specific method for determining this threshold follows the ASTM D5470 standard thermal resistance test procedure. The critical heat flux density at which the material undergoes thermal breakdown is measured under junction temperature conditions; this value is derived from the connector manufacturer's specifications or from the material's critical thermal breakdown value obtained based on standards such as ASTM D5470. This is a safety margin factor, ranging from 0.3 to 0.5. The value of this factor is based on electronic component derating design specifications, such as GJB / Z35. The range of 0.3-0.5 corresponds to the first-level derating standard, ensuring sufficient thermal safety margin is maintained even as connector contact resistance increases with aging. The symbol used here is... The symbol used to represent heat flux density is used to distinguish it from the symbol used to represent heat flux power in Example 4. This ensures the uniqueness and accuracy of the physical parameter definitions; the specific calculation logic can quantify the impact of thermal shock on contact resistance, preventing the waterproof seal from failing or the terminals from melting due to overheating at the interface.

[0127] This embodiment extends the influence of the algorithm to the connector assembly; by using the heat flux density vector to guide the interface power limitation, it solves the connector thermal bottleneck problem that is often overlooked in traditional designs, ensuring the thermal safety of the entire photovoltaic energy storage link from the backsheet, battery to the interface, and eliminating the bottleneck effect in the system.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for configuring the energy storage capacity of a photovoltaic energy storage system, characterized in that, The specific steps include: Step 1: Obtain environmental meteorological data of the location of the photovoltaic module and the historical temperature curve of the photovoltaic panel; obtain the thermal property parameters of the thermally conductive insulation layer attached to the back of the photovoltaic module, and obtain the electrochemical parameters of the battery cells in the energy storage unit. The electrochemical parameters include at least internal resistance, specific heat capacity and maximum withstand temperature, and construct an initial parameter set. Step 2: Based on the initial parameter set, establish a heat flux intrusion model, using the photovoltaic panel as an external heat source, to simulate the conduction process of heat passing through the thermally conductive insulation layer into the energy storage unit cavity, and calculate the heat flux density vector that changes with time. Step 3: Construct a thermo-electrochemical coupling impedance network, using the heat flux density vector as an external heat source interference field. Combined with the internal resistance heat generation characteristics of the battery cell, solve the maximum current curve that the battery cell can output under the premise that the battery core temperature does not exceed the maximum tolerance temperature, and then obtain the dynamic thermal equilibrium capacity boundary. Step 4: Obtain user load demand data. Within the constraints of the dynamic thermal balance capacity boundary, iteratively correct the preset number of battery modules and the capacity of individual cells to generate the optimal battery pack capacity configuration scheme and the corresponding charge / discharge rate strategy.

2. The method of claim 1, wherein: Step one includes: S11. Collect temperature field data of the photovoltaic module backsheet within a preset time period using temperature sensors or meteorological databases to form historical temperature curves. S12. Obtain the physical properties of the thermally conductive insulating layer, specifically including the thermal conductivity and thickness. The thermal conductivity is selected as a value greater than or equal to 200 W / (m·K), and the thickness is selected as a value between 2 mm and 3 mm. S13. Obtain the internal resistance, specific heat capacity, and maximum withstand temperature of the battery cell; S14. Summarize the historical temperature curves, thermal conductivity, thickness, internal resistance, specific heat capacity, and maximum withstand temperature to generate an initial parameter set.

3. The method of claim 2, wherein: Step two includes: S21. Construct an equivalent circuit model for heat conduction, in which the backsheet of the photovoltaic module is mapped as a heat voltage source, the thermally conductive insulation layer is mapped as a thermal resistor, and the energy storage unit cavity is mapped as a thermal capacity node. S22、According to the formula Calculating the thermal resistance value of the thermally conductive insulation layer , as a damping coefficient in the thermal conduction equivalent circuit model, wherein is the thickness, is the thermal conductivity coefficient; S23. The historical temperature curve is used as an input source and loaded into the thermal conduction equivalent circuit model to calculate the heat flux flowing through the thermal resistance value and generate a heat flux density vector pointing to the energy storage unit cavity.

4. The method of claim 3, wherein the method further comprises: Step three includes: S31. Establish a heat generation model for a single battery cell and calculate the internal Joule heat of the battery based on the product of the square of the charging and discharging current and the internal resistance. S32. The heat flux density vector is superimposed with the internal Joule heat, and the battery core temperature evolution equation is constructed according to the law of conservation of energy. The equation describes the relationship between the battery temperature change rate and the net heat flow and specific heat capacity. S33. Set the maximum tolerable temperature as the boundary condition, and use the battery core temperature evolution equation to solve in reverse the maximum charge and discharge current allowed to pass at any time in order to keep the temperature within the limit. S34. Integrate the maximum charge and discharge current in the time domain to obtain the effective stored charge under thermal stress conditions, and form the dynamic thermal balance capacity boundary.

5. The method of claim 4, wherein: Step four includes: S41. Based on the user's load demand data, preset the initial number of parallel battery modules and the initial single-cell capacity, and calculate the theoretical load current required to meet the load demand. S42. Compare the theoretical load current with the maximum charge and discharge current corresponding to the dynamic thermal balance capacity boundary to generate a thermal safety assessment result. S43. If the thermal safety assessment results show that the theoretical load current is greater than the maximum charge and discharge current at any time, it is determined to be out of bounds and a derating correction operation is performed. This is done by increasing the number of battery modules connected in parallel to spread the current or by replacing the cells with thinner ones to reduce thermal resistance, until the theoretical load current is less than or equal to the maximum charge and discharge current throughout the entire time period. S44. If the thermal safety assessment results show that the theoretical load current is less than or equal to the maximum charge and discharge current throughout the entire period, it is deemed qualified. The current number of parallel battery modules and the capacity of individual cells are locked, and the optimal battery pack capacity configuration scheme is output.

6. The method of claim 5, wherein: Step four also includes: S45. Based on the optimal battery pack capacity configuration scheme, simulate the full life cycle degradation curve of the battery under the thermal environment of the photovoltaic backsheet; S46. Determine whether the full life cycle decay curve meets the preset cycle life index. S47. If the preset cycle life index is not met, increase the redundancy capacity configuration of the battery module and repeat steps S42 to S44 until the preset cycle life index is met.

7. The method of claim 1, wherein: The charge / discharge rate strategy generated in step four is configured as follows: When the historical temperature curve of the photovoltaic panel shows that the temperature is higher than the preset high temperature threshold, the charging rate of the battery pack is limited to the first C-rate value; When the historical temperature curve of the photovoltaic panel shows that the temperature is less than or equal to the preset high temperature threshold and greater than the preset normal temperature threshold, the charging rate of the battery pack is limited to the second C-rate value. When the historical temperature curve of the photovoltaic panel shows that the temperature is less than or equal to the preset normal temperature threshold, the charging rate of the battery pack is restored to the rated C-rate value. Wherein, the high temperature threshold is greater than the normal temperature threshold, the first C-rate value is less than the second C-rate value, and the second C-rate value is less than the rated C-rate value.

8. The method of claim 3, wherein the method further comprises: determining the energy storage capacity of the photovoltaic energy storage system based on the energy storage capacity of the photovoltaic energy storage system and the energy storage capacity of the energy storage system. The thermally conductive insulating layer is an aluminum nitride ceramic sheet, and the heat flux density vector calculated in step two is used to guide the power limit setting of the IP67 quick-connect interface between the energy storage unit and the photovoltaic module.

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