Energy storage battery temperature control early warning system

Abstract

The invention relates to an energy storage battery temperature control early warning system, which belongs to the technical field of energy storage power station control, and comprises a power grid load dynamic prediction unit, a battery thermal characteristic digital twinning unit, a battery thermal characteristic digital twinning unit, a battery thermal characteristic digital twinning unit and a battery thermal characteristic digital twinning unit, the energy storage power curve prediction unit is used for receiving the predicted energy storage power curve and driving an electric-thermal coupling simulation model to perform simulation processing in combination with the current battery state obtained from the battery management system and the temperature control system operation power fed back by the temperature control system refined execution unit to obtain the predicted maximum temperature difference, and the temperature control early-warning and decision-making unit is used for receiving the predicted maximum temperature difference and sending the predicted maximum temperature difference to the battery management system. The preset temperature control system response time is combined, the thermal risk index model is adopted for calculation processing, and the thermal risk index is obtained.Adjustment is intervened in advance, response delay is eliminated, local overheating of the battery is effectively avoided, and thermal runaway is prevented.

Description

Technical Field

[0001] This invention relates to the field of energy storage power station control technology, specifically to an energy storage battery temperature control and early warning system. Background Technology

[0002] When large-scale energy storage power stations participate in high-frequency dynamic frequency regulation of the power grid, the battery system needs to withstand high-rate, bidirectional power surges. This operating condition exacerbates battery heat generation and leads to significant temperature differences between individual cells within the module. As is known to those skilled in the art, the maximum temperature difference between individual cells within a battery module (ΔT) is... max Temperature is a core indicator affecting the overall lifespan and safety of battery clusters. If it frequently exceeds critical thresholds such as 8°C, it will trigger the "barrel effect" within the battery cluster, leading to irreversible premature capacity decay and lifespan reduction.

[0003] Existing temperature control technologies are mostly passive response modes, that is, high-power cooling is only activated after the temperature or temperature difference exceeds the limit. Due to the inherent delay in heat transfer, such solutions have the risk of response lag and temperature difference runaway. Moreover, the crude control strategy of simply turning on and off results in huge parasitic energy consumption of the temperature control system, which affects the overall operating efficiency of the energy storage power station. Therefore, there is a profound technical contradiction between ensuring temperature control performance and reducing parasitic energy consumption.

[0004] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an energy storage battery temperature control and early warning system to solve the problems mentioned in the background art.

[0006] The technical solution of the present invention includes: a power grid load dynamic prediction unit, used to process historical energy storage power data and real-time power grid correction factors using a dynamic weighted power prediction model to obtain a predicted energy storage power curve;

[0007] A digital twin unit for battery thermal characteristics is used to receive the predicted energy storage power curve, combine the current battery state obtained from the battery management system with the operating power of the temperature control system fed back by the temperature control system fine execution unit, drive the electro-thermal coupling simulation model to perform simulation processing, and obtain the predicted maximum temperature difference.

[0008] The temperature control early warning and decision-making unit is used to receive the predicted maximum temperature difference, combine it with the preset temperature control system response time, and use the thermal risk index model to calculate and process the thermal risk index.

[0009] The temperature control system's refined execution unit receives the thermal risk index, uses a proportional-integral control law for adjustment, obtains the temperature control system's control power, and feeds back the temperature control system's control power as the temperature control system's operating power to the battery's thermal characteristic digital twin unit.

[0010] Preferably, the processing procedure of the dynamic weighted power prediction model is as follows:

[0011] The historical energy storage power data is weighted and summed with the corresponding dynamic weights, and the weighted summation result is superimposed with the real-time correction factor of the power grid to obtain the predicted energy storage power curve.

[0012] Preferably, the dynamic weights are periodically and adaptively adjusted based on the prediction error of the previous period using an online learning algorithm.

[0013] Preferably, the processing procedure of the battery thermal characteristic digital twin unit includes:

[0014] S1. Based on the predicted energy storage power curve, the Coulomb integral method is used to process the data to obtain the predicted state of charge.

[0015] S2. Combining the predicted state of charge, the battery equivalent circuit model is used to process the data to obtain the predicted terminal voltage and predicted current.

[0016] S3. Based on the predicted current and predicted state of charge, the Bernardi battery heat generation model is used to process the data and obtain the heat generation rate of each individual cell.

[0017] S4. Using the heat generation rate as the internal heat source and the operating power of the temperature control system as the active heat dissipation term, we input them into the single cell temperature evolution model for simulation processing to obtain the predicted temperature of each single cell; and based on the predicted temperature of each single cell, we obtain the predicted maximum temperature difference.

[0018] Preferably, the processing procedure of the temperature control early warning and decision-making unit includes:

[0019] Based on the temperature evolution information output by the battery thermal characteristic digital twin unit, the temperature control buffer time required from the current moment until the predicted maximum temperature difference first reaches the preset maximum temperature difference threshold is calculated.

[0020] Preferably, the processing procedure of the temperature control early warning and decision-making unit further includes:

[0021] Determine the temperature difference risk level, which is the ratio of the predicted maximum temperature difference to the critical value of the maximum temperature difference;

[0022] Determine the time risk level, which is the ratio of the temperature control system response time to the temperature control buffer time;

[0023] The thermal risk index is obtained by weighting and summing the temperature difference risk and the time risk.

[0024] Preferably, the adjustment process of the proportional-integral control law is as follows:

[0025] The deviation between the thermal risk index and the preset target risk value is obtained, and proportional and integral calculations are performed on the deviation. The calculation results are then superimposed with the preset base operating power to obtain the control power of the temperature control system.

[0026] This invention provides an improved energy storage battery temperature control and early warning system, which has the following improvements and advantages compared with the prior art:

[0027] Firstly, this invention achieves a fundamental shift in temperature control strategy from passive response to proactive management, significantly improving the operational safety of energy storage power stations. By setting up a grid load dynamic prediction unit and adopting a dynamic weighted power prediction model, it is possible to predict energy storage power impacts in the short time domain in advance. The prediction results drive the electro-thermal coupling simulation model in the battery thermal characteristic digital twin unit, thereby deducing the future temperature evolution trend. This design enables the system to anticipate risks before temperature difference exceeds limits, thereby intervening in advance to adjust and eliminate response delays, effectively avoiding local overheating of the battery and preventing thermal runaway.

[0028] Secondly, this invention introduces a decision-making mechanism for quantifying risks, making temperature control more precise and reasonable. It has innovatively constructed a temperature control early warning and decision-making unit, enabling the system to make differentiated and more reasonable responses to different levels of potential thermal risks based on the magnitude of the thermal risk index, thus avoiding unnecessary overcooling or insufficient intervention.

[0029] Thirdly, this invention achieves refined and efficient energy-saving temperature control, significantly reducing parasitic energy consumption of energy storage power stations. This invention sets up a refined execution unit for the temperature control system, which solves the energy loss caused by frequent start-stop and excessive cooling in traditional control methods. Under the premise of reliably maintaining the maximum single-cell temperature difference of the battery module within the safe threshold, it can significantly reduce the parasitic energy consumption of the temperature control system, thereby improving the overall operating efficiency and economy of the energy storage power station.

[0030] Fourth, this invention effectively suppresses temperature inconsistencies within the battery cluster, thereby extending the overall service life of energy storage assets. Significant temperature differences between individual cells within a battery module are the core factor causing the "weakest link" effect in the battery cluster, leading to irreversible capacity degradation. This invention effectively suppresses temperature inconsistencies within the battery cluster through a closed-loop control system of prediction-simulation-decision-execution-feedback. By combining forward-looking prediction of the maximum temperature difference—a core indicator—with refined control, this system mitigates the premature degradation of individual cells caused by temperature differences, ensuring the consistency of the overall performance of the battery cluster, and ultimately significantly extending the service life and value of energy storage assets. Attached Figure Description

[0031] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0032] Figure 1 This is a flowchart of an energy storage battery temperature control and early warning system according to the present invention. Detailed Implementation

[0033] 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.

[0034] Example 1:

[0035] Please see Figure 1 The present invention provides an energy storage battery temperature control early warning system, including: a grid load dynamic prediction unit, which is used to process historical energy storage power data and real-time grid correction factors using a dynamic weighted power prediction model to obtain a predicted energy storage power curve;

[0036] A digital twin unit for battery thermal characteristics is used to receive the predicted energy storage power curve, combine the current battery state obtained from the battery management system with the operating power of the temperature control system fed back by the temperature control system fine execution unit, drive the electro-thermal coupling simulation model to perform simulation processing, and obtain the predicted maximum temperature difference.

[0037] The temperature control early warning and decision-making unit is used to receive the predicted maximum temperature difference, combine it with the preset temperature control system response time, and use the thermal risk index model to calculate and process the thermal risk index.

[0038] The temperature control system's refined execution unit receives the thermal risk index, uses a proportional-integral control law for adjustment, obtains the temperature control system's control power, and feeds back the temperature control system's control power as the temperature control system's operating power to the battery's thermal characteristic digital twin unit.

[0039] The core of the energy storage battery temperature control and early warning system disclosed in this embodiment lies in a closed-loop control architecture consisting of a grid load dynamic prediction unit, a battery thermal characteristic digital twin unit, a temperature control early warning and decision-making unit, and a temperature control system fine-grained execution unit. In this architecture, the grid load dynamic prediction unit forms the logical starting point for the entire forward-looking control, and its output predicted energy storage power curve provides a crucial data foundation for subsequent units. Based on this prediction data, the battery thermal characteristic digital twin unit drives an electro-thermal coupling simulation model that runs synchronously with the physical battery system to accurately predict future battery temperature difference evolution trends. The temperature control early warning and decision-making unit then calculates the temperature control system based on the prediction results using a risk quantification model. The thermal risk index guides the final control action. Based on this risk index, the refined execution unit of the temperature control system generates a smooth and accurate power command and applies it to the temperature control hardware. At the same time, the power command is sent back to the digital twin unit as a feedback signal, thus forming a complete prediction-simulation-decision-execution-feedback closed loop. This architecture aims to fundamentally solve the problems of delayed temperature control response and excessive parasitic energy consumption caused by the inherent delay of heat transfer when large energy storage power stations participate in high-frequency dynamic frequency regulation of the power grid. The technical effect is that it can reliably control the maximum single-cell temperature difference in the battery module within a preset safety threshold, such as within 8°C, under harsh dynamic frequency regulation conditions, while significantly reducing the overall operating energy consumption of the temperature control system.

[0040] Example 2

[0041] The processing procedure for the dynamic weighted power prediction model is as follows:

[0042] The historical energy storage power data is weighted and summed with the corresponding dynamic weights, and the weighted summation result is superimposed with the real-time correction factor of the power grid to obtain the predicted energy storage power curve.

[0043] The dynamic weights are periodically and adaptively adjusted based on the prediction error of the previous period through an online learning algorithm.

[0044] In this embodiment, the function of the power grid load dynamic prediction unit is realized through a dynamically weighted power prediction model, the mathematical expression of which is as follows: The model is designed to provide a lightweight and fast-responding second-level power prediction mechanism for large-scale energy storage power stations that need to participate in high-frequency dynamic frequency regulation of the power grid. Its underlying logic lies in integrating the periodic patterns of historical data with the instantaneous randomness of power grid frequency regulation commands; where P pred (t+Δt p ) represents the value of time t in the future Δt. p The predicted energy storage power at any given time is the direct output of the model; P hist (t-(i-1)Δt hist) represents the actual power value of the energy storage system at the i-th historical sampling point before the current time t; w i (t) represents the dynamic weight of the i-th historical data point. It is a dimensionless core adjustable parameter. This weight is periodically and adaptively adjusted based on the prediction error of the previous period through an online learning algorithm, such as the least mean square algorithm, so that the historical data segment with better prediction effect receives higher weight. All weights are normalized to 1; t: the current time; i: the index of the historical data point, which is the loop variable.

[0045] Taking online learning algorithms, such as the normalized least mean square algorithm, as an example, in each prediction step Δt p At the end, the actual power value P is obtained. actual After (t), the prediction error e(t-Δt) from the previous step can be calculated. p The update rule for the weight vector W(t) can be expressed as:

[0046]

[0047] In the formula, W(t)=[w1(t),w2(t),...,w N (t)] T It is the weight vector, P hist (t-Δt p The weight vector represents the historical power data vector from the previous time step, and μ is the step size factor that determines the convergence speed and stability. The updated weight vector needs to be normalized to ensure... This method allows the weights to be quickly and adaptively adjusted based on the latest predicted performance; e(t-Δt) p ): The prediction error of the previous step; ||P hist (t-Δt p )|| 2 : L2 norm of historical power data vector, Euclidean norm, is a standard mathematical notation; T: standard mathematical notation for vector transpose, used to convert a row vector into a column vector;

[0048] δ grid (t) is the real-time correction factor for the power grid. This parameter is calculated by the power plant energy management system based on the real-time deviation of the power grid frequency to compensate for the prediction lag caused by relying solely on historical data.

[0049] This correction factor can be generated by a proportional controller, and the calculation formula is as follows:

[0050] δ grid (t)=K f ·(f ref -f actual (t))

[0051] In the formula, f actual (t) represents the real-time frequency of the power grid, f ref K is the reference frequency of the power grid, for example, 50Hz. f For frequency deviation correction gain, this gain parameter can be pre-calibrated according to the specific response characteristics of the power grid and the regulation capability of the energy storage power station, where K f The dimension of this term is power / frequency, and the unit is W / Hz. This correction term enables the predicted power to respond quickly to the real-time demand of the power grid; f ref The reference frequency of the power grid; f actual (t): Real-time frequency of the power grid; K f Frequency deviation correction gain;

[0052] N is the sliding window size representing the number of historical data points; Δt p With Δt hist These represent the prediction step size and the historical data sampling interval, respectively. In system applications, the grid load dynamic prediction unit collects historical power sequences and obtains real-time correction factors from the EMS. Using the aforementioned model, it calculates and outputs the energy storage power curve P for the future short-term time domain. pred The accurate prediction of (t) is achieved. The dynamic weight adaptation capability of this prediction mechanism ensures that the model can be continuously optimized. The predicted power curve output by the model provides a data foundation for the entire system to achieve forward-looking control, ensuring the predictability of subsequent temperature control decisions from the source.

[0053] Example 3

[0054] The processing steps for the digital twin unit of battery thermal characteristics include:

[0055] S1. Based on the predicted energy storage power curve, the Coulomb integral method is used to process the data to obtain the predicted state of charge.

[0056] S2. Combining the predicted state of charge, the battery equivalent circuit model is used to process the data to obtain the predicted terminal voltage and predicted current.

[0057] S3. Based on the predicted current and predicted state of charge, the Bernardi battery heat generation model is used to process the data and obtain the heat generation rate of each individual cell.

[0058] S4. Using the heat generation rate as the internal heat source and the operating power of the temperature control system as the active heat dissipation term, we input them into the single cell temperature evolution model for simulation processing to obtain the predicted temperature of each single cell; and based on the predicted temperature of each single cell, we obtain the predicted maximum temperature difference.

[0059] In this embodiment, the function of the battery thermal characteristic digital twin unit is to establish an accurate mapping from predicted power to predicted temperature difference. This process is achieved through a multi-step coupled model that includes electric state of charge prediction, heat generation rate calculation and temperature evolution simulation. There are close logical and data transfer relationships between each step.

[0060] To perform electric state-of-charge prediction, based on the predicted current I pred (t), predicting the state of charge (SoC) using the Coulomb integral method. pred The evolution of state of charge (SoC); SoC is a dimensionless parameter representing the ratio of current charge to rated capacity; the calculation formula is: Where Δt: time step, which, depending on the context, is the simulation time interval; Q rated,As The rated capacity of the battery, measured in ampere-seconds (A·s); SoC(t) is the current SoC value obtained from the battery management system, and is dimensionless; Q rated,As The rated capacity of the battery is expressed in ampere-seconds (A·s). If the rated capacity Q... rated If the unit is ampere-hour (Ah), then the formula is expressed as follows: This formula calculates the change in SoC using the Coulomb integral method, and this change is also dimensionless; based on this, the prediction result is Q. rated The rated capacity of a battery, measured in ampere-hours (Ah); using the battery's equivalent circuit model U... pred (t)=U ocv (SoC pred (t))-I pred (t)·R int,j (T j (t),SoC pred (t) is used to predict the battery's terminal voltage U. pred (t), where U ocv The open-circuit voltage of the battery is the SoC. pred The function, R int,j The internal impedance of a single cell is related to temperature T. j and SoC pred Related, open-circuit voltage function U ocv (SoC) and internal impedance function R int,j (T j The SoC (System-on-a-Chip) is fixed for a specific cell model and can be calibrated using standard experimental testing methods. For example, using hybrid pulse power characteristic testing, a series of charge-discharge pulse tests are performed on the battery at different ambient temperatures and state of charge (SOC). By analyzing the voltage response data, the U can be fitted. ocv and R int,j Lookup tables or empirical functions related to temperature and SoC; j: index of a single cell;

[0061] To calculate the heat production rate, the predicted current I obtained in the previous step is used. pred (t) and the updated state parameters are used in the Bernardi battery heat generation model Q. gen,j (t)=I pred (t) 2 R int,j (T j SoC pred (t))+ The model is processed; it can accurately describe the Joule heat and reaction heat of the battery, and is the basis for building a high-fidelity thermal model; where Q gen,j (t) represents the instantaneous heat production rate of monomer j at time t; I pred (t) represents the predicted current obtained after the previous iteration; R int,j (T j SoC pred (t) represents the internal impedance of monomer j, which is the current temperature T. j and predicting state of charge SoC pred The function; T is the entropy-heat coefficient of monomer j; j (t) represents the real-time temperature of cell j at time t, obtained from the BMS, and is used to calculate the entropy-heat term in the Bernardi battery heat generation model. j (t) must be measured using an absolute temperature scale, with units in Kelvin (K); T: based on the entropy coefficient of heat. In this context, T represents absolute temperature;

[0062] The entropy-heat coefficient in this model This can also be determined experimentally, by measuring the open-circuit voltage U of the battery cell at different temperatures. ocv The mass m of a single cell is obtained by calculating the rate of change of voltage with temperature. j Its specific heat capacity c can be obtained by direct weighing. p It can be measured using equipment such as an accelerating rate calorimeter;

[0063] To perform temperature evolution simulation, the calculated heat production rate Q will be used. gen,j (t) serves as the internal heat source, and the operating power P of the temperature control system fed back by the execution unit... tcs The corresponding heat dissipation power Q cool,j (t) is used as the active heat dissipation term and substituted into the single-cell temperature evolution model. This model directly links control behavior to thermodynamic state, solving the problem of decoupling heat dissipation capacity and control output in traditional models; where T j,pred (t+Δt) represents the predicted temperature of monomer j at a future time Δt; m j and c pThese are the mass and specific heat capacity of monomer j, respectively; Q cool,j The total heat dissipation power of (t) is determined by the current actual operating power P of the temperature control system. tcs (t current The decision is the key feedback input for achieving a closed-loop control system; m j : The mass of a single cell j; c p : Specific heat capacity of a single cell j; Q cool,j (t): Active heat dissipation power corresponding to the operating power of the temperature control system;

[0064] From the system operating power P tcs To the heat dissipation power Q of a single unit cool,j The transformation relationship can be modeled as follows:

[0065] Q cool,j (t)=η cop ·P tcs (t)·β j

[0066] In the formula, η cop The overall energy efficiency ratio (OER) of a temperature control system, such as an air conditioner or cooling unit, converts the input electrical power into total cooling power. This parameter can be calibrated through performance testing of the temperature control hardware. β j Let be the heat dissipation distribution coefficient for cell j, representing the proportion it receives from the total cooling capacity; this coefficient is related to the flow field distribution inside the battery module, and for all cells, ∑β j =1; β j The value can be obtained through computational fluid dynamics simulation or experimental calibration by attaching multiple temperature sensors to the battery module; this model precisely correlates the macroscopic control power with the microscopic individual heat dissipation effect; η cop Overall energy efficiency ratio of the temperature control system; P tcs (t): Current actual operating power of the temperature control system; β j : Heat dissipation distribution coefficient of single cell j;

[0067] Through the above steps, the digital twin unit receives the predicted power, combines the real-time status of the BMS with the actual operating power of the temperature control system, simulates the temperature evolution trajectory of each individual cell in the short time domain, and finally outputs the predicted maximum temperature difference ΔT. max,pred This process provides high-precision, future-oriented temperature difference data for subsequent decision-making, enabling the system to make decisions based on scientific predictions of the future.

[0068] Example 4

[0069] The processing steps of the temperature control early warning and decision-making unit include:

[0070] Based on the temperature evolution information output by the battery thermal characteristic digital twin unit, the temperature control buffer time required from the current moment until the predicted maximum temperature difference first reaches the preset maximum temperature difference critical value is calculated;

[0071] The processing steps of the temperature control early warning and decision-making unit also include:

[0072] Determine the temperature difference risk level, which is the ratio of the predicted maximum temperature difference to the critical value of the maximum temperature difference;

[0073] Determine the time risk level, which is the ratio of the temperature control system response time to the temperature control buffer time;

[0074] The thermal risk index is obtained by weighting and summing the temperature difference risk and the time risk.

[0075] In this embodiment, the temperature control early warning and decision-making unit, as the core decision-making module of the system, receives the predicted maximum temperature difference ΔT from the digital twin unit. max,pred The decision is processed based on a custom thermal risk index model; the core decision model is expressed as follows: The design logic of this model is that temperature control decisions should not rely solely on predicting the peak temperature difference, but must also take into account the urgency of the time required for the system to respond to the risk. This elevates the decision-making process from a single-dimensional threshold judgment to a comprehensive quantitative assessment of the urgency of the risk. th (t): Dimensionless comprehensive thermal risk index; ω T : Dimensionless weighting factor; ΔT max,pred (t+τ response Predict the maximum temperature difference; ΔT crit : Maximum temperature difference critical value; ω τ : Dimensionless weighting factor; τ response Temperature control system response time; τ buffer (t): Temperature control buffer time;

[0076] Where R th (t) represents the dimensionless comprehensive thermal risk index output, which is the sole input to the subsequent control module; the first term The temperature difference risk level is determined by the predicted maximum temperature difference ΔT. max,pred (t+τ response ) and the maximum temperature difference critical value ΔT crit The ratio is determined, and this ΔT crit This is a threshold set based on battery safety and lifespan requirements, using experimental data or industry standards, such as 8°C; The second item... The time-related risk level is determined by the response time τ of the temperature control system. response With temperature control buffer time τ buffer The ratio of (t) is determined, where τresponse The inherent response time, τ, is obtained by calibrating the hardware system through a step response test. buffer (t) is the temperature evolution curve calculated by this unit based on the output of the digital twin unit, from the current time to ΔT. max,pred First time reaching the critical value ΔT crit Time required; ω T With ω τ These are dimensionless weighting factors, and their sum is 1. They are used to adjust the conservatism or economy of the decision-making strategy, and their ratio can be optimized in the simulation according to the operating objectives of the power plant.

[0077] In system applications, this unit calculates the temperature control buffer time τ. buffer (t) is used to determine the temperature difference risk level and the time risk level, and the thermal risk index R is obtained by weighted summation. th (t); This index provides a quantitative scientific basis for subsequent refined adjustments, enabling control decisions to simultaneously consider both the magnitude of the risk and the urgency of the time.

[0078] Example 5

[0079] The adjustment process of the proportional-integral control law is as follows:

[0080] The deviation between the thermal risk index and the preset target risk value is obtained, and proportional and integral calculations are performed on the deviation. The calculation results are then superimposed with the preset base operating power to obtain the control power of the temperature control system.

[0081] In this embodiment, the function of the temperature control system's refined execution unit is to process the risk index R output by the decision unit. th (t) is transformed into precise and continuous power control commands for the temperature control hardware. The adjustment process is achieved through a proportional-integral (PI) control law; the mathematical expression of this control law is: The technical reason for adopting this control law is that it replaces the traditional on / off control with a regulator that can output continuous and smooth power, thereby achieving a precise match between heat dissipation energy output and heat dissipation demand, maximizing energy savings while ensuring temperature control effect.

[0082] Where P tcs (t) represents the final output control power applied to the temperature control system hardware at time t, measured in watts (W); P base The base operating power (W) necessary to maintain the system's minimum cycle length; R th (t) represents the real-time thermal risk index input by the decision-making unit, which is dimensionless; R th,target K represents an ideal steady-state target value below the risk trigger threshold, for example, 0.5, dimensionless; pThe proportional gain, measured in watts (W), determines the strength of the controller's response to the current risk deviation; K i The integral gain, measured in watts per second (W / s), is responsible for eliminating steady-state errors; K p and K i These two gain parameters can be calibrated during system integration using engineering tuning methods such as Ziegler-Nichols; P base Basic operating power; R th,target : Target risk value for an ideal steady state; K p : Proportional gain; K i : Integral gain; τ: Integral variable, representing time within the integration interval, is the standard symbol in calculus;

[0083] In system applications, this execution unit continuously receives the thermal risk index R. th (t), and calculate its relationship with the target risk value R. th,target The deviation; based on this deviation, the PI controller generates a precise regulating power value through proportional and integral calculations, which is then added to the base operating power to form the final temperature control system control power P. tcs (t); This power command directly drives the temperature control device to achieve fine adjustment of the cooling intensity; as the operating power of the temperature control system, it is fed back to the digital twin unit of the battery thermal characteristics in real time for the next round of simulation correction; this closed-loop feedback ensures that every prediction of the system can take into account the actual impact of the previous control action, thereby achieving dynamic, adaptive predictive control, effectively avoiding temperature overshoot, and maintaining the temperature consistency of the battery cluster within a safe range at a low energy cost.

[0084] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An energy storage battery temperature control warning system, characterized in that, The application relates to a battery thermal management system, comprising: a power grid load dynamic prediction unit for obtaining a predicted energy storage power curve by processing historical energy storage power data and a real-time grid correction factor based on a dynamic weighted power prediction model; a battery thermal characteristic digital twin unit for receiving the predicted energy storage power curve, combining current battery states obtained from a battery management system and a temperature control system operating power fed back by a temperature control system fine execution unit, driving an electro-thermal coupling simulation model to perform simulation processing to obtain a predicted maximum temperature difference; a temperature control early warning and decision unit for receiving the predicted maximum temperature difference, combining a preset temperature control system response time, and performing calculation processing on a thermal risk index model to obtain a thermal risk index; a temperature control system fine execution unit for receiving the thermal risk index, performing adjustment processing on a proportional-integral control law to obtain temperature control system control power, and feeding back the temperature control system control power to the battery thermal characteristic digital twin unit as the temperature control system operating power.

2. The energy storage battery temperature control warning system of claim 1, wherein, The processing process of the dynamic weighted power prediction model is as follows: The historical energy storage power data and the corresponding dynamic weight are weighted and summed, and the weighted sum result is superimposed with the real-time grid correction factor to obtain the predicted energy storage power curve.

3. The energy storage battery temperature control warning system of claim 2, wherein, The dynamic weight is periodically self-adaptively adjusted according to the prediction error of the last period by an online learning algorithm.

4. The energy storage battery temperature control warning system of claim 1, wherein, The processing process of the battery thermal characteristic digital twin unit comprises: S1, based on the predicted energy storage power curve, the coulomb integral method is used for processing to obtain a predicted state of charge; S2, combining the predicted state of charge, the battery equivalent circuit model is used for processing to obtain a predicted terminal voltage and a predicted current; S3, based on the predicted current and the predicted state of charge, the Bernard battery heat generation model is used for processing to obtain the heat generation rate of each single battery; S4, the heat generation rate is taken as an internal heat source, and the temperature control system operating power is taken as an active heat dissipation item, which is substituted into a single battery temperature evolution model for simulation processing to obtain the predicted temperature of each single battery; and based on the predicted temperature of each single battery, the predicted maximum temperature difference is obtained.

5. The energy storage battery temperature control warning system of claim 1, wherein, The processing process of the temperature control early warning and decision unit comprises: According to the temperature evolution information output by the battery thermal characteristic digital twin unit, the temperature control buffer time required from the current time to the first time when the predicted maximum temperature difference reaches the preset maximum temperature difference critical value is calculated.

6. The energy storage battery temperature control warning system of claim 5, wherein, The processing process of the temperature control early warning and decision unit further comprises: determining a temperature difference risk degree, which is the ratio of the predicted maximum temperature difference to the maximum temperature difference critical value; determining a time risk degree, which is the ratio of the temperature control system response time to the temperature control buffer time; performing weighted sum processing on the temperature difference risk degree and the time risk degree to obtain the thermal risk index.

7. The energy storage battery temperature control warning system of claim 1, wherein, The adjustment processing process of the proportional-integral control law is as follows: obtaining the deviation of the thermal risk index and a preset target risk value, performing proportional operation and integral operation on the deviation, and superimposing the operation result with a preset basic operating power to obtain the temperature control system control power.

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