An electrothermal coupling driven active support control method, system, device and medium for energy storage

Abstract

This invention discloses an electrothermal coupling driven active support control method, system, device, and medium for energy storage, belonging to the field of power energy storage system control technology. The method involves: when there is a demand for active support from the power grid, using a preset electrothermal dynamic coupling prediction model, combined with acquired dynamic internal resistance and dynamic thermal resistance, to deduce the temperature rise trajectory of the energy storage battery; and based on the temperature rise trajectory and preset thermal safety constraints, performing a reverse solution to obtain the maximum allowable support power; generating an active support control command based on the maximum allowable support power, and sending the active support control command to the energy storage converter to execute the active support task of the power grid. Therefore, by implementing this invention, it is possible to achieve forward-looking prediction of the future temperature rise trajectory of the energy storage battery, providing the system with a safe and maximized dynamic power boundary, thereby fully tapping the energy storage potential and ensuring the continuity and stability of the power grid support task.

Description

Technical Field

[0001] This invention relates to the field of power energy storage system control technology, and in particular to an electrothermal coupling driven active support control method, system, device and medium for energy storage. Background Technology

[0002] Currently, to address the lack of inertia in power systems with a high proportion of renewable energy, large-scale energy storage systems need to have the ability to actively support the power grid, i.e., to provide rapid power response with high speeds and short-term power during grid frequency or voltage fluctuations. Existing energy storage control systems typically adopt a discrete management architecture: the battery management system (BMS) obtains the state of power (SOP) limit through static lookup based on the current battery state of charge (SOC) and the highest single-cell temperature; the power storage converter (PCS) receives this limit and executes dispatch commands; and the thermal management system (TMS) operates independently, only passively increasing heat dissipation when the temperature exceeds the limit.

[0003] However, existing technologies have the following drawbacks: First, poor safety. Relying solely on the current temperature for static limiting makes it impossible to predict the temperature rise caused by thermal inertia and cumulative effects in the next few seconds of high-power commands. This makes the battery highly susceptible to triggering the BMS hard trip protection due to sudden temperature rises when performing active grid support tasks, causing the support task to fail midway or even leading to system safety accidents. Second, large assessment errors. Existing technologies treat electrical parameters (internal resistance) and thermal parameters (thermal resistance) separately and consider them both as fixed values. In reality, the battery's internal resistance changes nonlinearly with temperature and aging state (SOH), and the heat dissipation thermal resistance also drifts dynamically with cooling conditions. Existing technologies cannot identify these time-varying parameters online, resulting in significant deviations in the estimation of the battery's available power capacity. Third, low equipment utilization. Due to the lack of forward-looking prediction capabilities for future temperature rises, a large safety margin is forced to be reserved in static lookup tables to prevent thermal runaway. This results in the energy storage system being unable to output the required support power, severely limiting the release of active support potential and causing a waste of energy storage assets. Summary of the Invention

[0004] This invention provides an electrothermal coupling driven active support control method, system, device and medium for energy storage, which can realize the forward prediction of the future temperature rise trajectory of energy storage batteries, provide the system with a safe and maximized dynamic power boundary, thereby fully tapping the energy storage potential and ensuring the continuity and stability of grid support tasks.

[0005] This invention provides an active support control method for energy storage driven by electrothermal coupling, comprising: Based on the operating status data of the energy storage battery, obtain the dynamic internal resistance and dynamic thermal resistance of the energy storage battery. When there is an active grid support demand, the temperature rise trajectory of the energy storage battery is deduced by using a preset electrothermal dynamic coupling prediction model, combined with the dynamic internal resistance and the dynamic thermal resistance. The maximum allowable support power is obtained by inversely solving the temperature rise trajectory in combination with preset thermal safety constraints. The electrothermal dynamic coupling prediction model includes an electrical heat generation sub-model and a thermodynamic sub-model. The predicted temperature value output by the thermodynamic sub-model is used to update the internal resistance parameter in the electrical heat generation sub-model in real time to form a closed-loop coupling. Active support control commands are generated based on the maximum allowable support power, and these commands are sent to the energy storage converter to execute the grid active support task.

[0006] This invention, through the simulation of the temperature rise trajectory of energy storage batteries, achieves forward-looking prediction of future temperature changes, overcoming the limitations of traditional methods that rely solely on current temperature for post-hoc judgment. By using the predicted temperature value output by the thermodynamic sub-model to update the internal resistance parameters in the electrical heat generation sub-model in real time, a closed-loop coupling mechanism is formed, simulating a real physical positive feedback process, significantly improving the accuracy of temperature rise prediction and power boundary assessment. Based on the temperature rise trajectory and preset thermal safety constraints, a reverse solution is performed to obtain the maximum allowable support power, and an active support control command is generated based on this power and sent to the energy storage converter to achieve dynamic output of the maximum power that can be safely carried under the current state, thereby fully tapping the active support potential of the energy storage system. Compared with the problems of poor safety, large assessment errors, and low equipment utilization caused by static table lookup based on current temperature, separation of electrical and thermal parameters, and large conservative margins in the prior art, this invention obtains the maximum allowable support power through a dynamic electrothermal coupling prediction model combined with thermal safety constraints, fully tapping the energy storage potential and significantly improving the power release capacity and operating economy of energy storage assets.

[0007] Furthermore, when there is an active grid support demand, the temperature rise trajectory of the energy storage battery is deduced through a preset electrothermal dynamic coupling prediction model, combined with the dynamic internal resistance and the dynamic thermal resistance. Then, based on the temperature rise trajectory and preset thermal safety constraints, a reverse solution is performed to obtain the maximum allowable support power, specifically: When there is a demand for active support from the power grid, a hypothetical output power is selected within a preset power search range through a prediction time window, and the hypothetical output power is input into a preset electrothermal dynamic coupling prediction model. Combining the dynamic internal resistance and the dynamic thermal resistance, a forward deduction is performed through the electrothermal dynamic coupling prediction model to obtain the temperature rise trajectory corresponding to the hypothetical output power. Each temperature rise trajectory is compared with a preset thermal safety constraint, and the maximum allowable support power is obtained by iterative search in reverse. The maximum allowable support power is the assumed output power corresponding to the temperature of the temperature rise trajectory that is closest to the boundary of the thermal safety constraint at the end of the prediction time window.

[0008] By constructing a dynamic electrothermal coupling prediction model and introducing dynamic internal resistance and dynamic thermal resistance parameters, the forward extrapolation of the temperature rise trajectory of the energy storage battery is achieved. This upgrades the power decision-making under the active support demand of the power grid to predictive planning, significantly improving the safety and foresight of the power response. By iteratively comparing and solving the temperature rise trajectory within the predicted time window with the preset thermal safety constraints, the maximum allowable support power that just reaches the thermal safety boundary at the end of the time window is accurately located. This forms a closed-loop optimization strategy from forward verification to reverse delimitation, which fully releases the short-term overload capacity of the energy storage system under the premise of ensuring thermal safety and maximizes the active support potential of the energy storage converter.

[0009] Furthermore, the electrical heat generation sub-model is used to calculate the total heat generation rate of the energy storage battery based on the current and temperature data in the operating status data, combined with the heat generation impedance obtained from the dynamic internal resistance and the temperature coefficient of the open-circuit voltage of the energy storage battery. The total heat production rate is input into the thermodynamic sub-model to drive the thermodynamic sub-model to predict the temperature.

[0010] Furthermore, the thermodynamic sub-model is used to dynamically calculate the heat balance relationship between heat accumulation and heat dissipation of the energy storage battery based on the battery specific heat capacity, temperature data, total heat generation rate and dynamic thermal resistance in the operating state data, so as to predict the future temperature change of the energy storage battery. The future temperature change is fed back to the electrical heat generation sub-model in real time, the internal resistance parameter of the electrical heat generation sub-model that changes with temperature is updated, and the complete temperature rise trajectory is obtained through iterative deduction.

[0011] Furthermore, based on the operating status data of the energy storage battery, the dynamic internal resistance and dynamic thermal resistance of the energy storage battery are obtained, specifically: Based on the operating status data of the energy storage battery and combined with the preset equivalent circuit model, the dynamic internal resistance of the energy storage battery is obtained by iterative calculation using the recursive least squares method. Based on the operating status data of the energy storage battery and combined with the preset heat generation and dissipation differential equations, the dynamic thermal resistance of the energy storage battery is obtained by iterative calculation using the recursive least squares method.

[0012] By collecting operational status data of the energy storage battery and combining it with the equivalent circuit model and the heat generation and dissipation differential equations, the dynamic internal resistance and dynamic thermal resistance can be identified online in real time. Iterative calculations are performed using the recursive least squares method, and the parameter estimates are continuously updated using historical data and the latest sampled values, so that the estimated values ​​of internal resistance and thermal resistance are adaptively adjusted in real time according to factors such as battery aging, temperature changes and state of charge fluctuations.

[0013] Furthermore, the difference equation is obtained by discretizing the differential equation, specifically as follows: Based on the first-order thermal network model of the energy storage battery, the differential equations for heat generation and dissipation are obtained. The differential equations are then discretized using the Euler method to obtain a difference equation with the sampling period as the step size. The difference equation contains the iterative relationship between the initial thermal resistance data and the battery specific heat capacity and temperature data in the operating state data.

[0014] Furthermore, after the active support control command is sent to the energy storage converter, the following steps are also included: Based on the temperature rise trajectory, it is determined whether the future temperature of the energy storage battery will exceed the preset temperature threshold. If so, a strong cooling mode command is sent to increase the coolant flow or fan speed in advance, taking advantage of the lag in thermal management to gain more power headroom. If not, send an energy-saving mode command to maintain the current normal state.

[0015] By issuing a strong cooling mode command in advance when the future temperature will exceed the preset temperature threshold, the coolant flow or fan speed is actively increased. This fully utilizes the thermal inertia hysteresis effect of the thermal management system, giving the energy storage battery extra power carrying time and space under the same thermal safety constraints. This further increases the upper limit of short-term active support power while ensuring safety. When the future temperature is within the safe range, an energy-saving mode command is sent or the normal state is maintained, avoiding long-term high-power operation of the thermal management system, reducing auxiliary energy consumption and extending the life of cooling components. This significantly improves the overall operating efficiency and economy of the energy storage system in grid active support scenarios.

[0016] Another embodiment of the present invention provides an electrothermal coupling driven active support control system for energy storage, comprising: an energy storage dynamic data acquisition module, a support power acquisition module, and an active support control execution module; The energy storage dynamic data acquisition module is used to acquire the dynamic internal resistance and dynamic thermal resistance of the energy storage battery based on the operating status data of the energy storage battery. The power acquisition module is used to, when there is an active grid support demand, use a preset electrothermal dynamic coupling prediction model, combined with the dynamic internal resistance and the dynamic thermal resistance, to deduce the temperature rise trajectory of the energy storage battery, and then perform a reverse solution based on the temperature rise trajectory and preset thermal safety constraints to obtain the maximum allowable support power; wherein, the electrothermal dynamic coupling prediction model includes an electrical heat generation sub-model and a thermodynamic sub-model; the predicted temperature value output by the thermodynamic sub-model is used to update the internal resistance parameter in the electrical heat generation sub-model in real time to form a closed-loop coupling; The active support control execution module is used to generate active support control commands based on the maximum allowable support power, and send the active support control commands to the energy storage converter to execute the grid active support task.

[0017] This invention, through the simulation of the temperature rise trajectory of energy storage batteries, achieves forward-looking prediction of future temperature changes, overcoming the limitations of traditional methods that rely solely on current temperature for post-hoc judgment. By using the predicted temperature value output by the thermodynamic sub-model to update the internal resistance parameters in the electrical heat generation sub-model in real time, a closed-loop coupling mechanism is formed, simulating a real physical positive feedback process, significantly improving the accuracy of temperature rise prediction and power boundary assessment. Based on the temperature rise trajectory and preset thermal safety constraints, a reverse solution is performed to obtain the maximum allowable support power, and an active support control command is generated based on this power and sent to the energy storage converter to achieve dynamic output of the maximum power that can be safely carried under the current state, thereby fully tapping the active support potential of the energy storage system. Compared with the problems of poor safety, large assessment errors, and low equipment utilization caused by static table lookup based on current temperature, separation of electrical and thermal parameters, and large conservative margins in the prior art, this invention obtains the maximum allowable support power through a dynamic electrothermal coupling prediction model combined with thermal safety constraints, fully tapping the energy storage potential and significantly improving the power release capacity and operating economy of energy storage assets.

[0018] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the electrothermal coupling driven active support control method for energy storage as described in the present invention.

[0019] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform steps such as the electrothermal coupling driven active support control method for energy storage of the present invention. Attached Figure Description

[0020] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the electrothermal coupling driven active support control method for energy storage provided by the present invention. Figure 2 This is a block diagram of the principle of the electro-thermal coupling model of another embodiment of the electrothermal coupling driven active support control method for energy storage provided by the present invention. Figure 3 This is a schematic diagram of the online evaluation and control architecture of another embodiment of the electrothermal coupling driven active support control method for energy storage provided by the present invention; Figure 4 This is a schematic diagram of the calculation of temperature rise trajectory and power boundary in the prediction time domain of another embodiment of the electrothermal coupling driven active support control method for energy storage provided by the present invention. Figure 5 This is a schematic diagram of another embodiment of the electrothermal coupling driven active support control system for energy storage provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0024] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0027] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0028] See Figure 1 and Figure 2 To address the problem of active support control for energy storage in existing technologies, an embodiment of the present invention provides an electrothermal coupling driven active support control method for energy storage, comprising steps S1 to S3, the specific steps of which are as follows: S1. Obtain the dynamic internal resistance and dynamic thermal resistance of the energy storage battery based on the operating status data of the energy storage battery. S2. When there is a demand for active grid support, the temperature rise trajectory of the energy storage battery is deduced by using a preset electrothermal dynamic coupling prediction model, combined with the dynamic internal resistance and the dynamic thermal resistance. The maximum allowable support power is obtained by inversely solving the temperature rise trajectory in combination with the preset thermal safety constraints. The electrothermal dynamic coupling prediction model includes an electrical heat generation sub-model and a thermodynamic sub-model. The predicted temperature value output by the thermodynamic sub-model is used to update the internal resistance parameter in the electrical heat generation sub-model in real time to form a closed-loop coupling. The electrical heat generation sub-model is used to calculate the total heat generation rate of the energy storage battery based on the current and temperature data in the operating status data, combined with the heat generation impedance obtained from the dynamic internal resistance and the temperature coefficient of the open-circuit voltage of the energy storage battery. The total heat generation rate is then input into the thermodynamic sub-model to drive it to predict temperature. The thermodynamic sub-model is used to dynamically calculate the heat balance relationship between heat accumulation and heat dissipation of the energy storage battery based on the battery specific heat capacity, temperature data, the total heat generation rate, and the dynamic thermal resistance in the operating status data, in order to predict future temperature changes of the energy storage battery. The future temperature changes are fed back to the electrical heat generation sub-model in real time to update the internal resistance parameters of the electrical heat generation sub-model that change with temperature, and a complete temperature rise trajectory is obtained through iterative deduction.

[0029] The specific formula for the electrical heat generation sub-model is as follows: in, Q gen Total heat production rate; I For input current; T The current temperature; R dyn The internal resistance is ohmic, and the internal resistance is at temperature. T The function; This is the temperature coefficient of the battery open-circuit voltage, used to calculate the heat of reversible reaction; The specific formulas for the thermodynamic sub-model are as follows: in, T This represents the actual measured temperature of the battery at the corresponding moment. C th The specific heat capacity of the battery is considered a constant. R th For real-time thermal resistance; Total heat production rate; T amb The ambient temperature; The temperature at the next moment output by the thermodynamic sub-model T(k+1) This will be immediately fed back to the electrical heat generation sub-model to update the internal resistance. R ohm This forms a closed-loop coupled feedback path of "current → heat generation → temperature rise → internal resistance change → heat generation change"; S3. Generate an active support control command based on the maximum allowable support power, and send the active support control command to the energy storage converter to execute the grid active support task.

[0030] In practical applications, the maximum allowable support power is used as the power limit value; if the active support control value is less than the power limit value, the full power is output; otherwise, the maximum allowable support power is output to ensure that the battery does not experience thermal runaway.

[0031] As an example of an embodiment of the present invention, such as Figure 3 As shown, in practical applications, the electrothermal coupling driven active support control method for energy storage, as described in this invention, is implemented through physical layer devices and control layer units. The physical layer device's energy storage battery cluster, composed of several individual battery cells connected in series and parallel, serves as the carrier for energy storage and release. The energy storage converter connects the battery cluster to the power grid, responsible for AC / DC power conversion and power regulation. The thermal management system includes a liquid-cooled unit or air-cooled fan, cooling pipes, and temperature sensors for heat dissipation from the battery cluster. The sensor group includes a voltage transformer, a current transformer (collecting battery terminal voltage U and current I), an individual cell temperature sensor (collecting battery surface temperature Ts), and an ambient temperature sensor (collecting air inlet or coolant inlet temperature Tamb). The control layer unit is the core carrier of this invention and is typically integrated into the main control unit of an energy management system (EMS) or battery management system (BMS). The controller is connected to the aforementioned physical devices via a communication bus (such as CAN bus or Modbus TCP); the data acquisition unit of the control layer unit is used to read data from the sensor group in real time; the online identification unit is used to calculate the real-time equivalent internal resistance and thermal resistance; the coupling prediction unit is used to run the electrothermal coupling model to predict future temperatures; and the decision control unit is used to calculate the maximum allowable power and send it to the PCS.

[0032] In one embodiment, when there is an active grid support demand, a preset electrothermal dynamic coupling prediction model is used to deduce the temperature rise trajectory of the energy storage battery by combining the dynamic internal resistance and the dynamic thermal resistance. The maximum allowable support power is obtained by inversely solving the temperature rise trajectory in combination with the preset thermal safety constraints. This includes steps S201 to S202, each step of which is as follows: S201. When there is a demand for active support from the power grid, a hypothetical output power is selected within a preset power search range through a prediction time window, and the hypothetical output power is input into a preset electrothermal dynamic coupling prediction model. Combining the dynamic internal resistance and the dynamic thermal resistance, a forward deduction is performed through the electrothermal dynamic coupling prediction model to obtain the temperature rise trajectory corresponding to the hypothetical output power. S202. Compare each temperature rise trajectory with the preset thermal safety constraint, and perform reverse solution through iterative search to obtain the maximum allowable support power; wherein, the maximum allowable support power is the assumed output power corresponding to the predicted temperature of the temperature rise trajectory being closest to the boundary of the thermal safety constraint at the end of the prediction time window.

[0033] In practical applications, see Figure 4 The horizontal axis represents future time. t (From the current moment) t 0 By the prediction deadline t 0 + t pred The vertical axis represents battery temperature. T The horizontal dashed line above represents the maximum allowable operating temperature of the battery. T limit (For example, 60℃); Curve A indicates excessive power, assuming output power... P 1 The prediction curve is in t 1 If the red line is touched too early, it means that the power cannot be sustained until the end of the prediction, which is unsafe; curve B indicates that the power is too low, assuming the output power is too low. P 2 The predicted temperature at the end of the forecast was far below the red line, indicating that the potential was not fully realized; curve C represents the critical optimum, assuming the output power... P max The prediction curve is in t pred The moment was just approaching the red line; The output power is calculated using either the bisection method or Newton's iteration method. P max Set the power search range [0, P rated The electrothermal dynamic coupling prediction model is used for rapid iterative simulation to find a value that makes EndTemperature ≈ T limit - δ The power value of (safety margin), that is, the power value corresponding to curve C, is the maximum active support capacity at the current moment.

[0034] This invention constructs an electrothermal dynamic coupling prediction model and introduces dynamic internal resistance and dynamic thermal resistance parameters to achieve forward extrapolation of the temperature rise trajectory of the energy storage battery. This upgrades the power decision-making under the active support demand of the power grid to predictive planning, significantly improving the safety and foresight of the power response. By iteratively comparing and solving the temperature rise trajectory within the predicted time window with the preset thermal safety constraints, the maximum allowable support power that just reaches the thermal safety boundary at the end of the time window is accurately located. This forms a closed-loop optimization strategy from forward verification to reverse delimitation, fully releasing the short-term overload capacity of the energy storage system while ensuring thermal safety, and maximizing the active support potential of the energy storage converter.

[0035] In one embodiment, the dynamic internal resistance and dynamic thermal resistance of the energy storage battery are obtained based on the battery's operating status data, including steps S301 to S302, each step of which is as follows: S301. Based on the operating status data of the energy storage battery and combined with the preset equivalent circuit model, the dynamic internal resistance of the energy storage battery is obtained by iterative calculation using the recursive least squares method. Specifically, the terminal voltage is obtained based on the simplified equivalent circuit model of the battery. U Open circuit voltage U ocv Current U ocv and internal resistance R dyn The relationship is as follows: Discretize it and reconstruct it into the RLS standard format, as follows: in, U ( t ), U ( k (V) represents the actual measured value of the battery's terminal voltage at the current moment. SOC , SOC ( k The value represents the battery's current state of charge, ranging from 0% to 100%. U ocv ( SOC ), U ocv ( k The open-circuit voltage (in V) of the battery at the current SOC is usually obtained by looking up a table using a preset OCV-SOC relationship curve. I ( t ), I ( k The value of the current charge / discharge current of the battery at the current moment is measured (unit: A). In this embodiment, the discharge current is defined as positive and the charging current as negative. R dyn ( t ), R dyn ( k ) represents the dynamic internal resistance of the battery to be identified (unit: Ω), which characterizes the combined ohmic polarization resistance under current temperature and aging conditions; y e ( k) represents the known observations in the electrical identification model, namely the difference between the open-circuit voltage and the terminal voltage; This represents the data matrix / regression vector in the electrical identification model, i.e., the measured current; θ e ( k ) represents the vector of parameters to be identified in the electrical identification model, i.e., the dynamic internal resistance; Update using the RLS iterative algorithm θ e ( k This allows you to obtain the dynamic internal resistance at the current moment in real time. R dyn ( k ).

[0036] S302. Based on the operating status data of the energy storage battery and combined with the first-order thermal network model of the energy storage battery, the differential equations for heat generation and dissipation are obtained. The differential equations are discretized using the Euler method to obtain the difference equations with the sampling period as the step size. The dynamic thermal resistance of the energy storage battery is obtained by iterative calculation using the recursive least squares method. The difference equations contain the iterative relationship between the initial thermal resistance data and the battery specific heat capacity and temperature data in the operating status data.

[0037] The specific formula for the differential equation of heat generation and dissipation is as follows: The heat generation and dissipation differential equations are discretized using the Euler method to obtain difference equations, as follows: Reconstruct it into the RLS standard format, as follows: The battery heat capacity and dynamic thermal resistance are obtained through inverse calculation, as shown in the following formula: in, T , T (k) T ( k -1) represents the actual measured internal / surface temperature of the battery at the corresponding moment (unit: K); T amb , T amb ( k -1) represents the ambient temperature or the temperature measurement of the thermal management cooling medium (unit: K); Cth Specific heat capacity / comprehensive heat capacity of the battery (unit: J / K), characterizing the thermal inertia of the battery absorbing heat and causing the temperature to rise; R th The overall dynamic thermal resistance (in K / W) of the battery to be identified to the environment characterizes the actual heat dissipation capacity under the current cooling system conditions (such as fan speed and fluid flow rate). y t ( k ) represents the known observation (scalar) in the thermal identification model, which represents the temperature rise change between two adjacent sampling periods; ϕ t ( k ) represents the data vector in the thermal identification model; θ t ( k ) represents the vector of parameters to be identified in the thermal identification model; a 1, a 2 represents the intermediate identification coefficient variable defined for convenient matrix operations; Δ t The sampling period; This is the index for the sampling time.

[0038] This invention collects the operating status data of the energy storage battery and combines it with the equivalent circuit model and the heat generation and dissipation differential equation to achieve online real-time identification of dynamic internal resistance and dynamic thermal resistance. It uses the recursive least squares method for iterative calculation and continuously updates the parameter estimates using historical data and the latest sampled values, so that the estimated values ​​of internal resistance and thermal resistance are adaptively adjusted in real time according to factors such as battery aging, temperature changes and state of charge fluctuations.

[0039] In one embodiment, after the active support control command is sent to the energy storage converter, steps S401 to S403 are further included, and the specific steps are as follows: S401. Based on the temperature rise trajectory, determine whether the future temperature of the energy storage battery will exceed the preset temperature threshold. S402, if so, send a strong cooling mode command to increase the coolant flow or fan speed in advance, and use the lag of thermal management to obtain a larger power margin. Among them, by sending a strong cooling mode command to the thermal management system (TMS), the coolant flow or fan speed is increased in advance, and the lag of thermal management is used to obtain a larger power margin. S403. If not, send an energy-saving mode command to maintain the current normal state.

[0040] In this way, by sending energy-saving mode commands to the thermal management system (TMS) or maintaining the current normal state (such as reducing fan speed, maintaining a low coolant flow rate, or entering standby hibernation), the energy consumption of auxiliary equipment of the energy storage system can be reduced to the greatest extent while ensuring battery thermal safety, thereby improving the overall operating energy efficiency of the power station.

[0041] This invention, by issuing a strong cooling mode command in advance when the future temperature will exceed a preset temperature threshold, actively increases the coolant flow or fan speed, fully utilizing the thermal inertia hysteresis effect of the thermal management system. Under the same thermal safety constraints, it gains additional power carrying time and space for the energy storage battery, thereby further increasing the upper limit of short-term active support power while ensuring safety. When the future temperature is within the safe range, it sends an energy-saving mode command or maintains the normal state, avoiding long-term high-power operation of the thermal management system, reducing auxiliary energy consumption and extending the life of cooling components, significantly improving the overall operating efficiency and economy of the energy storage system in grid active support scenarios.

[0042] like Figure 2 As shown in the electrothermal coupling driven active support control for energy storage, a corresponding system embodiment is provided based on the above method embodiment. This invention provides an electrothermal coupling driven active support control system for energy storage, including: an energy storage dynamic data acquisition module 501, a support power acquisition module 502, and an active support control execution module 503; The energy storage dynamic data acquisition module 501 is used to acquire the dynamic internal resistance and dynamic thermal resistance of the energy storage battery based on the operating status data of the energy storage battery. The supporting power acquisition module 502 is used to, when there is an active grid support demand, use a preset electrothermal dynamic coupling prediction model, combined with the dynamic internal resistance and the dynamic thermal resistance, to deduce the temperature rise trajectory of the energy storage battery, and perform reverse solving based on the temperature rise trajectory and preset thermal safety constraints to obtain the maximum allowable supporting power; wherein, the electrothermal dynamic coupling prediction model includes an electrical heat generation sub-model and a thermodynamic sub-model; the predicted temperature value output by the thermodynamic sub-model is used to update the internal resistance parameter in the electrical heat generation sub-model in real time to form a closed-loop coupling; The active support control execution module 503 is used to generate an active support control command based on the maximum allowable support power, and send the active support control command to the energy storage converter to execute the grid active support task.

[0043] This invention, through the simulation of the temperature rise trajectory of energy storage batteries, achieves forward-looking prediction of future temperature changes, overcoming the limitations of traditional methods that rely solely on current temperature for post-hoc judgment. By using the predicted temperature value output by the thermodynamic sub-model to update the internal resistance parameters in the electrical heat generation sub-model in real time, a closed-loop coupling mechanism is formed, simulating a real physical positive feedback process, significantly improving the accuracy of temperature rise prediction and power boundary assessment. Based on the temperature rise trajectory and preset thermal safety constraints, a reverse solution is performed to obtain the maximum allowable support power, and an active support control command is generated based on this power and sent to the energy storage converter to achieve dynamic output of the maximum power that can be safely carried under the current state, thereby fully tapping the active support potential of the energy storage system. Compared with the problems of poor safety, large assessment errors, and low equipment utilization caused by static table lookup based on current temperature, separation of electrical and thermal parameters, and large conservative margins in the prior art, this invention obtains the maximum allowable support power through a dynamic electrothermal coupling prediction model combined with thermal safety constraints, fully tapping the energy storage potential and significantly improving the power release capacity and operating economy of energy storage assets.

[0044] It is understood that the above system embodiments correspond to the method embodiments of the present invention, and can implement the electrothermal coupling driven active support control method for energy storage provided by any of the above method embodiments of the present invention.

[0045] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0046] For ease of description and brevity, the system embodiments of the present invention include all the embodiments of the above-described electrothermal coupling driven active support control method for energy storage, and will not be repeated here.

[0047] Based on the above embodiments of the electrothermal coupling driven active support control method for energy storage, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the electrothermal coupling driven active support control method for energy storage according to any embodiment of the present invention.

[0048] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0049] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0050] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0051] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the electrothermal coupling driven active support control method for energy storage described in any of the above-described method embodiments of the present invention.

[0052] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0053] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for active support control of energy storage driven by electrothermal coupling, characterized in that, include: Based on the operating status data of the energy storage battery, obtain the dynamic internal resistance and dynamic thermal resistance of the energy storage battery. When there is a demand for active grid support, a preset electrothermal dynamic coupling prediction model is used to deduce the temperature rise trajectory of the energy storage battery by combining the dynamic internal resistance and the dynamic thermal resistance. The maximum allowable support power is obtained by inversely solving the temperature rise trajectory in combination with the preset thermal safety constraints. The electrothermal dynamic coupling prediction model includes an electrical heat generation sub-model and a thermodynamic sub-model. The predicted temperature value output by the thermodynamic sub-model is used to update the internal resistance parameter in the electrical heat generation sub-model in real time to form a closed-loop coupling. Active support control commands are generated based on the maximum allowable support power, and these commands are sent to the energy storage converter to execute the grid active support task.

2. The electrothermal coupling driven active support control method for energy storage as described in claim 1, characterized in that, When there is an active grid support demand, a preset electrothermal dynamic coupling prediction model is used to deduce the temperature rise trajectory of the energy storage battery by combining the dynamic internal resistance and the dynamic thermal resistance. Then, based on the temperature rise trajectory and preset thermal safety constraints, a reverse solution is performed to obtain the maximum allowable support power. Specifically: When there is a demand for active support from the power grid, a hypothetical output power is selected within a preset power search range through a prediction time window, and the hypothetical output power is input into a preset electrothermal dynamic coupling prediction model. Combining the dynamic internal resistance and the dynamic thermal resistance, a forward deduction is performed through the electrothermal dynamic coupling prediction model to obtain the temperature rise trajectory corresponding to the hypothetical output power. Each temperature rise trajectory is compared with a preset thermal safety constraint, and the maximum allowable support power is obtained by iterative search in reverse. The maximum allowable support power is the assumed output power corresponding to the temperature of the temperature rise trajectory that is closest to the boundary of the thermal safety constraint at the end of the prediction time window.

3. The electrothermal coupling driven active support control method for energy storage as described in claim 1, characterized in that, The electrical heat generation sub-model is used to calculate the total heat generation rate of the energy storage battery based on the current and temperature data in the operating status data, combined with the heat generation impedance obtained from the dynamic internal resistance and the temperature coefficient of the open circuit voltage of the energy storage battery. The total heat production rate is input into the thermodynamic sub-model to drive the thermodynamic sub-model to predict the temperature.

4. The electrothermal coupling driven active support control method for energy storage as described in claim 3, characterized in that, The thermodynamic sub-model is used to dynamically calculate the heat balance relationship between heat accumulation and heat dissipation of the energy storage battery based on the battery specific heat capacity, temperature data, total heat generation rate and dynamic thermal resistance in the operating state data, so as to predict the future temperature change of the energy storage battery. The future temperature change is fed back to the electrical heat generation sub-model in real time, the internal resistance parameter of the electrical heat generation sub-model that changes with temperature is updated, and the complete temperature rise trajectory is obtained through iterative deduction.

5. The electrothermal coupling driven active support control method for energy storage as described in claim 1, characterized in that, The process of obtaining the dynamic internal resistance and dynamic thermal resistance of the energy storage battery based on its operating status data specifically involves: Based on the operating status data of the energy storage battery and combined with the preset equivalent circuit model, the dynamic internal resistance of the energy storage battery is obtained by iterative calculation using the recursive least squares method. Based on the operating status data of the energy storage battery and combined with the preset difference equation, the dynamic thermal resistance of the energy storage battery is obtained by iterative calculation using the recursive least squares method.

6. The electrothermal coupling driven active support control method for energy storage as described in claim 5, characterized in that, The difference equation is obtained by discretizing the differential equation, specifically as follows: Based on the first-order thermal network model of the energy storage battery, the differential equations for heat generation and dissipation are obtained. The differential equations are then discretized using the Euler method to obtain a difference equation with the sampling period as the step size. The difference equation contains the iterative relationship between the initial thermal resistance data and the battery specific heat capacity and temperature data in the operating state data.

7. The electrothermal coupling driven active support control method for energy storage as described in claim 1, characterized in that, After the active support control command is sent to the energy storage converter, the method further includes: Based on the temperature rise trajectory, it is determined whether the future temperature of the energy storage battery will exceed the preset temperature threshold. If so, a strong cooling mode command is sent to increase the coolant flow or fan speed in advance, taking advantage of the lag in thermal management to gain more power headroom. If not, send an energy-saving mode command to maintain the current normal state.

8. An electrothermal coupling driven active support control system for energy storage, characterized in that, include: Energy storage dynamic data acquisition module, support power acquisition module, and active support control execution module; The energy storage dynamic data acquisition module is used to acquire the dynamic internal resistance and dynamic thermal resistance of the energy storage battery based on the operating status data of the energy storage battery. The power acquisition module is used to, when there is an active grid support demand, use a preset electrothermal dynamic coupling prediction model, combined with the dynamic internal resistance and the dynamic thermal resistance, to deduce the temperature rise trajectory of the energy storage battery, and then perform a reverse solution based on the temperature rise trajectory and preset thermal safety constraints to obtain the maximum allowable support power; wherein, the electrothermal dynamic coupling prediction model includes an electrical heat generation sub-model and a thermodynamic sub-model; the predicted temperature value output by the thermodynamic sub-model is used to update the internal resistance parameter in the electrical heat generation sub-model in real time to form a closed-loop coupling; The active support control execution module is used to generate active support control commands based on the maximum allowable support power, and send the active support control commands to the energy storage converter to execute the grid active support task.

9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the electrothermal coupling driven active support control method for energy storage as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the electrothermal coupling driven active support control method for energy storage as described in any one of claims 1-7.

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