Energy storage system multi-time scale rolling optimization method, storage medium, processor

Abstract

This invention discloses a multi-timescale rolling optimization method, storage medium, and processor for energy storage systems, relating to the field of energy storage system technology. It solves the problem in existing technologies that rely solely on surface temperature monitoring, failing to detect and suppress accelerated aging in hot spots within battery cells. This invention first uses a heat conduction inversion algorithm to calculate the internal core temperature, which cannot be directly measured. Then, it converts the core temperature into an aging acceleration factor and maps it to a time-varying dynamic aging cost coefficient. Finally, by constructing a minimum objective function incorporating this dynamic aging cost, it uses model predictive control to solve for the optimal power and implements differentiated power allocation based on the internal temperature state of each battery cell. In summary, this invention achieves a technological leap from passive threshold protection to active optimization and suppression, ensuring that the energy storage system prioritizes the use of healthy battery cells while meeting grid dispatch requirements, fundamentally breaking the vicious cycle of hot spot aging.

Description

Technical Field

[0001] This invention relates to the field of energy storage system technology, and in particular to a method for multi-timescale rolling optimization of energy storage systems, a storage medium, and a processor. Background Technology

[0002] With the large-scale application of electrochemical energy storage technology in the grid side, user side, and new energy supporting fields, the safety and lifespan management of lithium-ion battery energy storage systems have become core technological bottlenecks restricting their economic viability. During the long-term operation of energy storage systems, the complex electrochemical reactions and thermodynamic processes inside the battery are coupled, inevitably leading to uneven temperature distribution within the cell. Since the heat generated inside the battery mainly originates from Joule heat generated by polarization resistance and ohmic resistance, while heat dissipation only occurs through heat exchange between the surface and the external environment, this asymmetry in heat generation and dissipation paths means that the temperature in the core area of ​​the cell is often significantly higher than the surface temperature, forming so-called "internal hot spots." In lithium iron phosphate and ternary lithium batteries, the temperature difference between the core and surface can reach 5°C to 15°C, and this temperature difference increases non-linearly with charge / discharge rate and aging degree. More importantly, battery aging follows the Arrhenius kinetic mechanism; for every 10°C increase in temperature, the chemical reaction rate approximately doubles. This means that the aging rate of the internal hot spot area may be 3 to 5 times that of the surface area. However, due to engineering obstacles such as high hardware cost, poor reliability, and manufacturing difficulties in embedding temperature sensors inside the battery cells, existing energy storage systems generally only place temperature monitoring points on the surface, making the internal thermal state a "black box." How to detect and suppress internal hot spots based solely on surface monitoring data, thereby delaying local aging and avoiding a sharp drop in overall capacity, has become a key technical problem that urgently needs to be solved in the field of energy storage system optimization and control.

[0003] Existing technologies rely solely on surface temperature monitoring and fixed threshold management, failing to detect and suppress accelerated aging in hotspot areas within the battery cell. Specifically, current thermal management strategies generally use cell surface temperature as the control basis, triggering air conditioning cooling or power limitation when the surface temperature exceeds a set threshold (e.g., 40°C). However, this method ignores the fundamental difference between surface temperature and core temperature. Due to the hysteresis and homogenization effect of surface temperature, when the surface temperature reaches the threshold, internal hotspots are often already severely overheated; conversely, when the surface temperature is normal, severe hotspots may already exist internally. More critically, existing power allocation strategies often employ simple SOC equalization or fixed rate limiting, lacking quantification methods for internal temperature non-uniformity, and failing to transform "suppressing hotspots" into an objective function recognizable by optimization algorithms. This "surface-centric, internal-neglecting" management approach causes hotspot areas to continuously endure high-rate charging and discharging stress, forming a vicious cycle of "hotspots → accelerated aging → increased internal resistance → intensified heat generation → even hotter," ultimately causing a precipitous drop in the overall capacity within a short period, severely shortening the lifespan of the energy storage system.

[0004] Therefore, there is a need for multi-timescale rolling optimization methods, storage media, and processors for energy storage systems. Summary of the Invention

[0005] To address the problem that existing technologies, which rely solely on surface temperature monitoring, cannot detect and suppress accelerated aging in hot spots within battery cells, this invention provides a multi-timescale rolling optimization method, storage medium, and processor for energy storage systems. This method overcomes hardware limitations by using a thermal conduction inversion algorithm to obtain the internal core temperature. It utilizes Arrhenius dynamics to transform hot spots into time-varying aging cost coefficients and constructs an objective function incorporating these dynamic costs. This achieves the technical effect of spontaneously suppressing internal hot spots and delaying localized aging through optimization algorithms without adding internal sensors, thus solving the problem of uncontrolled localized aging caused by lag in surface temperature monitoring. The specific technical solution is as follows: A multi-timescale rolling optimization method for energy storage systems includes the following steps: An infrared temperature array is arranged on the surface of the battery cluster to collect surface temperature data, as well as the total current, terminal voltage and ambient temperature of each battery cluster. Based on the cell surface temperature, the equivalent thermal resistance from the cell core to the surface, and the internal heat generation power of the cell, the internal core temperature of the cell is calculated using a heat conduction inversion algorithm, and then the difference between the internal core temperature and the surface temperature of the cell is calculated. The acceleration factor of local aging caused by hot spots is calculated based on the core temperature and temperature difference inside the battery cell. Multiply the aging acceleration factor by the unit capacity replacement cost to obtain the time-varying equivalent aging cost coefficient, and construct a cost minimization objective function that includes dynamic aging costs. The objective function is solved to obtain the maximum power, and the power command is differentiated based on the core temperature inside the battery cell.

[0006] Preferably, the calculation process for the core temperature inside the battery cell is as follows: In the formula, This refers to the core temperature inside the battery cell. The surface temperature of the battery cell. The equivalent thermal resistance from the cell core to the surface. This refers to the heat generated inside the battery cell.

[0007] Preferably, a hotspot aging acceleration factor is established using Arrhenius aging kinetics, as follows: In the formula, As an effective aging acceleration factor, As the activation energy of the battery cell, Let be the ideal gas constant. For reference temperature, This refers to the core temperature inside the battery cell. This represents the initial health state of the battery. The current battery health status, is the SOH sensitivity index, and is a dimensionless constant.

[0008] Preferably, the time-varying equivalent aging cost coefficient is as follows: In the formula, The time-varying dynamic aging cost coefficient. The cost per unit capacity of battery replacement is set based on current market prices. For reference cycle life, this refers to the total number of cycles a new battery can complete under standard operating conditions. It is an aging accelerating factor that changes in real time with temperature. This is the penalty coefficient for hot topics.

[0009] Preferably, the objective function is as follows: In the formula, For the first The total power of the energy storage system during a given time period, with positive values ​​representing discharge and negative values ​​representing charging, is the variable to be optimized. For the first Time-of-use electricity pricing for the power grid For the first Power purchased by the power grid during a given time period For the first Dynamic aging cost coefficient over a period of time.

[0010] Preferred, the first The calculation process for the power purchased by the power grid during a given time period is as follows: In the formula, For the first Load forecast data for different time periods For the first Photovoltaic forecast data for the specified time period.

[0011] Preferably, the solution process also includes constraints, which at least include hotspot hard constraints, specifically: If a certain battery cell Then force the cell Directly remove it.

[0012] Preferably, the differentiated allocation of power commands includes at least: The total power demand is prioritized for battery cells without hot spots; For battery cells that are detected as hotspots, limit their charge / discharge rate or temporarily disconnect them.

[0013] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the multi-timescale rolling optimization method for energy storage systems as described above.

[0014] A processor for running a program, wherein the program executes the multi-timescale rolling optimization method for energy storage systems as described above.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention first utilizes a heat conduction inversion algorithm to calculate the internal core temperature, which cannot be directly measured, based on surface temperature array data, an equivalent thermal resistance model, and real-time heat generation power. This overcomes the physical limitations of hardware monitoring and obtains real-time distribution information of internal hotspots. Next, based on Arrhenius aging kinetics, the core temperature is transformed into an aging acceleration factor and mapped to a time-varying dynamic aging cost coefficient, converting the severity of hotspots at the physical level into power usage costs. Finally, by constructing a minimization objective function incorporating this dynamic aging cost, model predictive control is used to solve for the optimal power. Differential power allocation is implemented based on the internal temperature state of each cell, allowing the optimization algorithm to spontaneously transfer power from hotspot cells to low-temperature cells. This deep integration of thermal state perception and optimization achieves a technological leap from passive threshold protection to active optimization and suppression, ensuring that the energy storage system prioritizes the use of healthy battery cells while meeting grid dispatch requirements, avoiding high-risk hotspot areas, and fundamentally breaking the vicious cycle of hotspot aging. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that, when used in this specification, the terms “comprising” and “including” indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0020] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” are intended to include the plural forms.

[0021] It should also be further understood that the term "and / or" as used in this specification refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0022] In one embodiment of the present invention, a multi-timescale rolling optimization method for an energy storage system is provided, comprising the following steps: Step 1: An infrared temperature array is arranged on the surface of the battery cluster to collect surface temperature data, as well as the total current, terminal voltage and ambient temperature of each battery cluster.

[0023] After data acquisition, basic data preprocessing is performed, including spatial interpolation of surface temperature data and low-pass filtering of current and voltage data to eliminate high-frequency sampling noise, thereby outputting real-time electrical operating parameters.

[0024] Step 2: Based on the cell surface temperature, the equivalent thermal resistance from the cell core to the surface, and the internal heat generation power of the cell, the internal core temperature of the cell is calculated using a heat conduction inversion algorithm, and then the difference between the internal core temperature and the surface temperature of the cell is calculated.

[0025] The calculation process for the core temperature inside the battery cell is as follows: In the formula, This refers to the core temperature inside the battery cell. The surface temperature of the battery cell. The equivalent thermal resistance from the cell core to the surface. This refers to the heat generated inside the battery cell.

[0026] For example, the average surface temperature of the corresponding area of ​​each cell is taken as the surface temperature.

[0027] For example, the internal heat generation power of the battery cell is simplified by using Joule heating.

[0028] The temperature difference is calculated as follows: Step 3: Substitute the core temperature and temperature difference inside the cell output in Step 2 into the Arrhenius aging kinetic equation to calculate the local aging acceleration factor caused by hot spots.

[0029] This step uses Arrhenius aging kinetics to establish hotspot aging acceleration factors. (Aging Factor Acceleration) is defined as the ratio of the aging rate at the current core temperature to the aging rate at the reference temperature. In the formula, As an effective aging acceleration factor, As the activation energy of the battery cell, Let be the ideal gas constant. For reference temperature, This refers to the core temperature inside the battery cell. This represents the initial health state of the battery. The current battery health status, is the SOH sensitivity index, and is a dimensionless constant.

[0030] In this embodiment, the reference temperature is taken as the average value of the surface temperature of all cells in the energy storage system at present.

[0031] In this embodiment, the initial health state of the battery is set to 100%, and the current battery health state is estimated in real time by the BMS through capacity testing or internal resistance growth method.

[0032] In this embodiment, the SOH sensitivity index is used to characterize the nonlinear characteristic of a sharp increase in temperature sensitivity during the later stages of aging. When SOH is less than a set threshold, this coefficient is significantly penalized, and the aging acceleration coefficient is exponentially weighted and amplified.

[0033] Step 4: Multiply the aging acceleration factor by the unit capacity replacement cost to obtain the time-varying equivalent aging cost coefficient; at the same time, when the aging acceleration factor of a certain cell exceeds the safety threshold, a penalty term is automatically triggered, forcing the optimization algorithm to avoid high-rate charging and discharging of the cell in subsequent scheduling.

[0034] This step first converts the physical aging rate (AFA) into a dynamic aging cost factor. This transforms the subsequent optimization algorithm from suppressing hotspots into a mathematical problem of minimizing costs. Specifically, the transformation process involves constructing a dynamic aging cost coefficient. In this embodiment, the dynamic aging cost coefficient represents the equivalent aging cost of the battery cell for every 1 kWh of charge / discharge in its current state, as detailed below: In the formula, The time-varying dynamic aging cost coefficient. The cost per unit capacity of battery replacement is set based on current market prices. For reference cycle life, this refers to the total number of cycles a new battery can complete under standard operating conditions. It is an aging accelerating factor that changes in real time with temperature. Here is the hotspot penalty coefficient, and here is a set constant.

[0035] In the above formula, As a secondary penalty, when Exceeding the set threshold When a state is considered to be in danger of becoming a hotspot, a secondary penalty will be triggered, creating a strong constraint.

[0036] Step 5: Construct a single-objective optimization function incorporating dynamic aging costs, in the form: Total Cost = Grid Purchase Cost + Dynamic Aging Cost Coefficient × Integral of Absolute Charge / Discharge Power + Hotspot Temperature Penalty. The dynamic aging cost coefficient uses the sequence value output in Step 4, ensuring that current power decisions directly impact future aging costs. A Model Predictive Control (MPC) framework is employed, with a 15-minute rolling period, to solve for the optimal power curve for the next 4 hours, ensuring that aging accumulation in hotspot areas is minimized while meeting grid dispatch requirements. Output: Optimal charge / discharge power curve of the energy storage system considering hotspot suppression.

[0037] The objective function is expressed as follows: In the formula, For the first The total power of the energy storage system during a given time period, with positive values ​​representing discharge and negative values ​​representing charging, is the variable to be optimized. For the first Time-of-use electricity pricing for the power grid For the first Power purchased by the power grid during a given time period For the first Dynamic aging cost coefficient over a period of time.

[0038] Among them, in order to maintain power balance, the first The calculation process for the power purchased by the power grid during a given time period is as follows: In the formula, For the first Load forecast data for different time periods For the first Photovoltaic forecast data for the specified time period.

[0039] In practical applications, 15 minutes is usually taken as a time period, and the solution is performed with a prediction time domain of 4 hours. That is, N is taken as 16, for a total of four hours, and the solution is performed in 15-minute cycles to generate the optimal charge and discharge power curve of energy storage considering hot spot suppression. This ensures that while meeting the grid dispatch requirements, low-temperature batteries are given priority and high-temperature batteries are avoided.

[0040] In addition, the following constraints are included in the solution process: 1. Power constraint: , This refers to the rated power of the energy storage system. 2. Energy Constraint (SOC Dynamics): ,in Hour (15 minutes) For rated capacity, For charge and discharge efficiency; 3. SOC range: ,generally ; 4. Hotspot hard constraint: If a certain battery cell Then force the cell Directly remove it.

[0041] A quadratic programming (QP) solver is used to solve the problem at the beginning of each control cycle, and only the optimal solution at the current time step is executed. The next cycle will be re-optimized to obtain a series of optimal power.

[0042] Step Six: Based on the optimal power output in Step 5, and combined with the estimated internal temperature field obtained in Step 2, differentiate the power command allocation. Specifically: The total power demand is preferentially allocated to battery cells with lower internal temperatures (no hot spots). For battery cells with detected hot spots, their charge / discharge rate is limited (e.g., limited to 0.5C), or even temporarily disconnected. The allocated power command is then sent to each battery cluster via the power conversion system (PCS).

[0043] In summary, this invention addresses the shortcomings of existing technologies that cannot detect internal hotspots leading to uncontrolled localized aging. This technical solution constructs a control system of "temperature sensing - aging quantification - economic optimization - differentiated execution," achieving proactive suppression and aging delay of internal hotspots without adding internal sensors. Specifically, this solution first utilizes a heat conduction inversion algorithm to calculate the internal core temperature, which cannot be directly measured, based on surface temperature array data, an equivalent thermal resistance model, and real-time heat generation power. This overcomes the physical limitations of hardware monitoring and obtains real-time distribution information of internal hotspots. Next, based on Arrhenius aging dynamics, the core temperature is transformed into an aging acceleration factor and mapped to a time-varying dynamic aging cost coefficient, converting the severity of hotspots at the physical level into power usage costs. Finally, by constructing a minimization objective function incorporating this dynamic aging cost, model predictive control is used to solve for the optimal power. Differentiated power allocation is implemented based on the internal temperature state of each cell, allowing the optimization algorithm to spontaneously transfer power from hotspot cells to low-temperature cells. This technology, which deeply integrates thermal state perception with economic optimization, achieves a technological leap from passive threshold protection to active optimization and suppression. It ensures that the energy storage system can meet the grid dispatch requirements while prioritizing the use of battery cells in good health and avoiding high-risk hotspot areas, thus fundamentally breaking the vicious cycle of hotspot aging.

[0044] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0045] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0046] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0047] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the specification of the present invention.

Claims

1. A multi-timescale rolling optimization method for an energy storage system, characterized in that, Includes the following steps: An infrared temperature array is arranged on the surface of the battery cluster to collect surface temperature data, as well as the total current, terminal voltage and ambient temperature of each battery cluster. Based on the cell surface temperature, the equivalent thermal resistance from the cell core to the surface, and the internal heat generation power of the cell, the internal core temperature of the cell is calculated using a heat conduction inversion algorithm, and then the difference between the internal core temperature and the surface temperature of the cell is calculated. The acceleration factor of local aging caused by hot spots is calculated based on the core temperature and temperature difference inside the battery cell. Multiply the aging acceleration factor by the unit capacity replacement cost to obtain the time-varying equivalent aging cost coefficient, and construct a cost minimization objective function that includes dynamic aging costs. The objective function is solved to obtain the maximum power, and the power command is allocated differently based on the core temperature inside the battery cell.

2. The multi-timescale rolling optimization method for an energy storage system according to claim 1, characterized in that, The calculation process for the core temperature inside the battery cell is as follows: In the formula, This refers to the core temperature inside the battery cell. The surface temperature of the battery cell. The equivalent thermal resistance from the cell core to the surface. This refers to the heat generated inside the battery cell.

3. The multi-timescale rolling optimization method for an energy storage system according to claim 1, characterized in that, Hotspot aging acceleration factors were established using Arrhenius aging kinetics, as detailed below: In the formula, As an effective aging acceleration factor, As the activation energy of the battery cell, Let be the ideal gas constant. For reference temperature, This refers to the core temperature inside the battery cell. This represents the initial health state of the battery. The current battery health status, is the SOH sensitivity index, and is a dimensionless constant.

4. The multi-timescale rolling optimization method for an energy storage system according to claim 1, characterized in that, The time-varying equivalent aging cost coefficient is as follows: In the formula, This is the time-varying dynamic aging cost coefficient. The cost per unit capacity of battery replacement is set based on current market prices. For reference cycle life, this refers to the total number of cycles a new battery can complete under standard operating conditions. It is an aging accelerating factor that changes in real time with temperature. This is the penalty coefficient for hot topics.

5. The multi-timescale rolling optimization method for an energy storage system according to claim 1, characterized in that, The objective function is as follows: In the formula, For the first The total power of the energy storage system during a given time period, with positive values ​​representing discharge and negative values ​​representing charging, is the variable to be optimized. For the first Time-of-use electricity pricing for the power grid For the first Power purchased by the power grid during a given time period For the first Dynamic aging cost coefficient over a period of time.

6. The multi-timescale rolling optimization method for an energy storage system according to claim 5, characterized in that, No. The calculation process for the power purchased by the power grid during a given time period is as follows: In the formula, For the first Load forecast data for different time periods For the first Photovoltaic forecast data for the specified time period.

7. The multi-timescale rolling optimization method for an energy storage system according to claim 1, characterized in that, The solution process also includes constraints, which at least include hotspot hard constraints, specifically: If a certain battery cell Then force the cell Directly remove it.

8. The multi-timescale rolling optimization method for an energy storage system according to claim 1, characterized in that, The differentiated allocation of power commands includes at least: The total power demand is prioritized for battery cells without hot spots; For battery cells that are detected as hotspots, limit their charge / discharge rate or temporarily disconnect them.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the multi-timescale rolling optimization method for energy storage systems according to any one of claims 1 to 8.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the multi-timescale rolling optimization method for energy storage systems according to any one of claims 1 to 8.

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