Sodium ion energy storage system dynamic load management method and system based on intelligent algorithm

Abstract

The invention discloses a sodium ion energy storage system dynamic load management method and system based on an intelligent algorithm, and relates to the technical field of battery management. Comprising the following steps: determining an initial thermal risk area through a thermal management model, obtaining a thermal risk score of each single battery at the same time, and determining a final thermal risk area from the initial thermal risk area; adjusting the thermal risk score through the state parameter of each single battery, and generating early warning information according to the adjusted thermal risk score; and according to the final thermal risk area and the early warning information, adjusting the charging and discharging rate and load distribution of each single battery. The state data of the single batteries are obtained through real-time monitoring, the working state of each single battery is accurately controlled, and it is ensured that the single batteries operate in the optimal working interval, so that unnecessary energy loss is reduced, and the energy conversion efficiency of the whole system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and specifically to a dynamic load management method and system for a sodium-ion energy storage system based on an intelligent algorithm. Background Art

[0002] With the increasing global demand for renewable energy, energy storage systems (ESS) play an increasingly important role in stabilizing power supply and improving grid flexibility. Sodium-ion batteries (SIB), as an alternative to lithium-ion batteries, have received extensive attention due to their abundant materials, low cost, and environmental friendliness. However, SIB still faces some challenges in practical applications, such as short cycle life and low energy density. Therefore, effective management and optimization strategies are needed to improve its performance.

[0003] The Chinese invention patent with publication number CN112732443A discloses an energy storage power station state evaluation and operation optimization system based on edge computing, including energy storage power station equipment, data communication and acquisition equipment, an edge computing device, and a cloud service platform; the energy storage power station equipment includes battery modules, a battery management system, an energy storage converter, and an energy storage energy management system; the data communication and acquisition equipment collects the operation data of the energy storage power station equipment and forwards the collected data to the edge computing device in real time, the edge computing device performs relevant data analysis locally and forwards the analysis results to the cloud service platform through the data communication and acquisition equipment, and the cloud service platform performs relevant data storage and further data optimization analysis. By constructing this system structure, rapid comprehensive evaluation and fault warning of the energy storage power station and its key components can be realized, operation plan support can be provided for the refined control management of the energy storage power station, and the pressure of real-time transmission of a large amount of data and communication between the cloud and the local can be reduced.

[0004] During the actual operation of the above-mentioned and similar energy storage systems, like sodium-ion batteries, when multiple single sodium-ion batteries are connected in series or parallel to form a large battery pack, due to slight differences in manufacturing processes and different material properties, there will be initial differences in voltage, capacity, internal resistance, etc. between each single battery. And as the usage time increases, these differences will further expand, resulting in some battery units aging prematurely due to excessive stress, and even potentially causing thermal runaway due to overcharging or over-discharging, thereby threatening the safety and lifespan of the entire system. Summary of the Invention

[0005] The purpose of the present invention is to provide a dynamic load management method and system for a sodium-ion energy storage system based on an intelligent algorithm to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions: A dynamic load management method for a sodium-ion energy storage system based on an intelligent algorithm, including:

[0007] Determine the thermal risk area: Through the thermal management model, determine the preliminary thermal risk area, and at the same time obtain the thermal risk score of each single battery. From the preliminary thermal risk area, determine the final thermal risk area;

[0008] Obtain warning information: Adjust the thermal risk score through the state parameters of each single battery, and at the same time generate warning information according to the adjusted thermal risk score;

[0009] Generate an adjustment strategy: According to the final thermal risk area and warning information, adjust the charge and discharge rates and load distribution of each single battery, including:

[0010] Reduce the charge and discharge rate: According to the warning information, within the optimal charge and discharge rate range of the sodium-ion battery, reduce the charge and discharge rate of the single battery;

[0011] Determine the load distribution: According to the final thermal risk area, determine the healthy single batteries and sub-healthy single batteries, and at the same time reduce the load of the sub-healthy single batteries and increase the load of the healthy single batteries.

[0012] Furthermore, the establishment process of the thermal management model includes:

[0013] Set up a sensor network: In the sodium-ion battery pack, set up a sensor network to obtain the state parameters of each single battery;

[0014] Set thermal parameters: Through the state parameters of the single battery, obtain the heat capacity parameter, thermal resistance parameter, and contact thermal resistance parameter. Specifically:

[0015]

[0016] Where: C th Is the heat capacity parameter, m is the weight of the single battery and its components, c p Is the specific heat capacity of the single battery and its components, R th,cond Is the thermal resistance parameter, L c Is the heat transfer distance between components, k c Is the thermal conductivity between components, A c Is the heat transfer area between components, R th，contact Is the contact thermal resistance parameter, ΔT is the temperature difference between two contact surfaces, q is the heat flow per unit time, A ct Is the contact area between different materials, k ct Is the thermal conductivity of the contact material, L ctis the effective thickness of the contact layer;

[0017] Construct a thermal management model: Through the heat capacity parameter, thermal resistance parameter, and contact thermal resistance parameter, obtain the energy conservation equation corresponding to the single-cell battery at each node, specifically:

[0018]

[0019] where: T i is the surface temperature of the i-th node, P i is the power generated inside the i-th node, Q in，i is the inflowing heat corresponding to the i-th node, Q out，i is the outflowing heat corresponding to the i-th node, C th，i is the heat capacity parameter corresponding to the i-th node, t is the time interval, is the rate of change of temperature with time at the i-th node.

[0020] Furthermore, the heat transfer mechanism during the operation of the single-cell battery includes conduction, convection, and radiation, specifically:

[0021]

[0022] where: Q cond is the heat transfer rate, k c is the thermal conductivity between components, A c is the heat transfer area between components, is the temperature gradient along the heat transfer path, ΔT is the temperature difference between two nodes, R th，cond is the thermal resistance parameter, Q conv is the surface heat dissipation energy, h is the convective heat transfer coefficient, A h is the heat dissipation area, T i is the surface temperature of the i-th node, T ∞ is the temperature of the fluid far from the object surface, Q rad is the radiative heat transfer energy, ε is the emissivity, σ is the Stefan-Boltzmann constant, A r is the radiation area, T i 4 is the absolute temperature of the surface of the i-th node, is the absolute temperature of the surface of the j-th node.

[0023] Furthermore, determine the final thermal risk area, including:

[0024] W1: Determine the safe temperature threshold range: According to the safe operating temperature range of the sodium-ion battery, determine the safe temperature threshold range;

[0025] W2: Determine the preliminary thermal risk area: Compare the real-time operating temperature of each single battery at each node with the safe temperature threshold range, and determine the preliminary thermal risk area according to the comparison result, specifically:

[0026] When the real-time operating temperature is not within the safe temperature threshold range, the area of the single battery at the node corresponding to the real-time operating temperature is the preliminary thermal risk area; otherwise, it is not the preliminary thermal risk area.

[0027] W3: Obtain the thermal risk score: According to the real-time operating temperature and the rate of change of temperature over time, obtain the thermal risk score corresponding to each single battery at each node, specifically:

[0028]

[0029] where: S i is the thermal risk score corresponding to the i-th node, ω 1 is the weight coefficient corresponding to the temperature, T i is the surface temperature of the i-th node, T avg is the average temperature of the i-th node, ω 2 is the weight coefficient corresponding to the rate of change, is the rate of change of temperature over time at the i-th node;

[0030] W4: Determine the final thermal risk area: Compare the thermal risk score corresponding to each single battery at each node with the score threshold, and determine the final thermal risk area from the preliminary thermal risk area according to the comparison result, specifically:

[0031] When the thermal risk score is not less than the score threshold, the area of the single battery at the node corresponding to the thermal risk score is the final thermal risk area; otherwise, it is not the final thermal risk area.

[0032] Furthermore, generate warning information, including:

[0033] Adjust the weight coefficient: Set the initial weight coefficient and adjust the initial weight coefficient through an adaptive algorithm;

[0034] Obtain the real-time thermal risk area: Update the thermal risk score formula through the adjusted weight coefficient and obtain the real-time thermal risk area;

[0035] Generate warning information: According to the thermal risk score corresponding to each real-time thermal risk area, compare the thermal risk score with the warning score, and at the same time, determine the alarm level corresponding to each real-time thermal risk area according to the comparison result, specifically:

[0036] When the thermal risk score is between 20 and 30, it is a first-level alarm, and an alarm prompt is issued at this time;

[0037] When the thermal risk score is between 30 and 40, it is a second-level alarm, and an automatic cooling measure is triggered at this time;

[0038] When the thermal risk score is greater than 40, it is a third-level alarm, and an emergency response measure is initiated at this time.

[0039] Furthermore, the initial weight coefficient is adjusted, including:

[0040] N1: Set the initial weight coefficient: According to the safe working range and operating data, set the initial weight coefficient;

[0041] N2: Determine the weight adjustment range: According to the initial weight coefficient, determine the adjustment range of the weight coefficient;

[0042] N3: Determine the weight coefficient to be adjusted: Compare the temperature rise rate and temperature obtained in real time with the warning value of the temperature rise rate and the safety upper limit of the temperature, and determine the weight coefficient to be adjusted according to the comparison result. Specifically:

[0043] When the temperature rise rate obtained in real time is greater than the warning value of the temperature rise rate, the change rate weight coefficient is increased; otherwise, the change rate weight coefficient remains unchanged;

[0044] When the temperature obtained in real time is greater than the safety upper limit of the temperature, the temperature weight coefficient is increased; otherwise, the temperature weight coefficient remains unchanged;

[0045] N4: Determine the final weight coefficient: Within the weight adjustment range, increase the weight coefficient to be adjusted according to the preset growth rate.

[0046] Furthermore, when the temperature rise rate obtained in real time is greater than the warning value of the temperature rise rate and the temperature obtained in real time is greater than the safety upper limit of the temperature, the temperature weight coefficient is increased.

[0047] A dynamic load management system for a sodium-ion energy storage system based on an intelligent algorithm, wherein the dynamic load management system for the sodium-ion energy storage system uses the dynamic load management method for a sodium-ion energy storage system based on an intelligent algorithm described in any one of the above.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] First: By obtaining the state data of the single battery through real-time monitoring, the present invention accurately controls the working state of each single battery and ensures its operation within the optimal working range, thereby reducing unnecessary energy loss and improving the energy conversion efficiency of the entire system;

[0050] Second: By dynamically adjusting the charge and discharge rates and load distribution, the present invention can effectively avoid situations harmful to the battery such as overcharging, over-discharging, and high temperature, thereby extending the service life of the battery and reducing the long-term operation cost.

[0051] Third: Through the real-time monitoring and warning mechanism, the present invention can promptly detect abnormal situations and take timely measures, effectively preventing potential safety risks from turning into actual hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic flow chart of the dynamic load management method for the sodium-ion energy storage system of the present invention;

[0053] Figure 2 It is a schematic flow chart for obtaining the final thermal risk area of the present invention;

[0054] Figure 3 It is a schematic flow chart for generating warning information of the present invention;

[0055] Figure 4 It is a schematic flow chart for adjusting the initial weight coefficient of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0057] During the actual operation of existing sodium-ion energy storage systems, similar to sodium-ion batteries, when multiple single sodium-ion batteries are connected in series or parallel to form a large battery pack, due to subtle differences in manufacturing processes and different material properties, initial differences in voltage, capacity, internal resistance, etc. will occur among individual batteries. Moreover, as the usage time increases, these differences will further expand, leading to premature aging of some battery units due to excessive stress, and even potentially causing thermal runaway due to overcharging or over-discharging, thereby threatening the safety and lifespan of the entire system. The technical solution of this application collects key operating parameters (such as temperature, voltage, current, etc.) of each single battery in the battery pack in real time through a sensor network, determines the thermal risk area and warning information based on the key operating parameters of each single battery, and simultaneously dynamically adjusts the charge and discharge rates and load distribution of each single battery according to the final thermal risk area and warning information, so as to ensure that all batteries operate within the optimal working range, thereby not only improving the overall energy efficiency and safety, but also extending the service life of the battery.

[0058] Reference Figures 1-4 , this embodiment provides a dynamic load management method for a sodium-ion energy storage system based on an intelligent algorithm. The dynamic load management method for the sodium-ion energy storage system includes the following steps:

[0059] Step S1: Determine the thermal risk area. That is, by setting up a sensor network, obtaining the sensor data of each single battery, and establishing a thermal management model based on the obtained sensor data to determine the preliminary thermal risk area. At the same time, obtain the thermal risk score of each single battery, and determine the final thermal risk area from the determined preliminary thermal risk area. Specifically as follows:

[0060] Step S1.1: Set up the sensor network. That is, in the sodium-ion battery pack, install temperature sensors (such as NTC thermistors) at key positions (such as electrodes and the surface of the outer shell) of each single battery. At the same time, set the temperature sensors to obtain the temperature data of the single battery at fixed time intervals (such as once per second), that is, read and record the data of all temperature sensors at a frequency of once per second.

[0061] Furthermore, by setting up a sensor network in the sodium-ion battery pack, the key state parameters of each single battery are monitored and obtained in real time, including but not limited to temperature, voltage, current, internal resistance, and temperature rise rate.

[0062] Step S1.2: Set thermal parameters. That is, obtain the heat capacity parameter, thermal resistance parameter, and contact thermal resistance parameter. In this embodiment, the heat capacity parameter is the ability of an object to change temperature when absorbing or releasing a certain amount of heat. Here, it refers to the heat capacity parameter corresponding to the single battery and its components (such as electrodes, diaphragms, etc.). In this embodiment, the heat capacity is calculated according to its material properties. Specifically:

[0063] C th = m·c p

[0064] Where: C th is the heat capacity parameter, m is the weight of the single cell and its components, and c p is the specific heat capacity of the single cell and its components.

[0065] In the process of specific implementation, for a standard 18650-type sodium-ion battery with a mass of 46.5 g and a specific heat capacity of 837 J / (kg·K), the corresponding heat capacity parameter C of this sodium-ion battery th is 38.9 J / K.

[0066] Furthermore, the thermal resistance parameter is the ratio of the temperature difference between the single cells at two nodes to the heat quantity passing through this path per unit time. Here, it refers to the thermal resistance value between components. In this embodiment, the thermal resistance parameter from the inside of the battery to the outer shell is specifically:

[0067]

[0068] Where: R th，cond is the thermal resistance parameter, L c is the heat transfer distance between components, k c is the thermal conductivity between components, A c is the heat transfer area between components.

[0069] In the process of specific implementation, the thermal conductivity of the sodium-ion battery is 0.5 W / (m·K), the heat transfer distance is 0.01 m, and the heat transfer area is 0.00314 m 2 (in this embodiment, it is a circular cross-section with a diameter of 18 mm), then the thermal resistance parameter R th，cond is 6.37 K / W.

[0070] Furthermore, the contact thermal resistance parameter is the interfacial effect between different materials. Here, it refers to the additional thermal resistance when the surfaces of different materials are in contact, specifically:

[0071]

[0072] Where: R th，contact is the contact thermal resistance parameter, ΔT is the temperature difference between two contact surfaces, q is the heat flow per unit time, A ct is the contact area between different materials, k ct is the thermal conductivity of the contact material, L ct is the effective thickness of the contact layer.

[0073] During the specific implementation process, the contact area between two metal contact surfaces is 0.00314 m 2 , the thermal conductivity of the contact material is 0.5 W / (m·K), and the effective thickness of the contact layer is 0.0001 m. Then, the contact thermal resistance parameter R th，contact is 62.8 K / W.

[0074] Step S1.3: Construct a thermal management model. That is, based on the thermal parameters set in Step S1.2, obtain the energy conservation equation corresponding to each single battery at each node. That is to say, through the constructed energy conservation equation, determine the change in the temperature of each single battery at each node over time. Specifically:

[0075]

[0076] where: T i is the surface temperature of the i-th node, P is the power generated inside the i-th node, Q in，i is the heat inflow corresponding to the i-th node, Q out，i is the heat outflow corresponding to the i-th node, C th，i is the heat capacity parameter corresponding to the i-th node, t is the time interval, is the rate of change of the temperature over time at the i-th node.

[0077] In this embodiment, the heat inflow Q in，i corresponding to the i-th node is the heat flowing from the single battery at other nodes into the single battery at the i-th node, and the heat outflow Q out，i corresponding to the i-th node is the heat flowing from the single battery at the i-th node to the single battery at other nodes. That is to say, through the heat inflow and heat outflow corresponding to the single battery at the i-th node, the net heat gain or loss corresponding to the single battery at the i-th node can be determined.

[0078] It should be noted that the heat inflow and heat outflow corresponding to the single battery at the i-th node can transfer heat to each other in the sodium-ion battery pack through three different heat transfer mechanisms: conduction, convection, and radiation. Specifically, in a sodium-ion energy storage system composed of multiple sodium-ion batteries, direct contact surfaces between adjacent batteries will cause heat conduction. That is to say, when heat is transferred by conduction, the formula for obtaining the heat transfer rate is specifically:

[0079]

[0080] where: Q cond is the heat transfer rate, k c is the thermal conductivity between components, A c is the heat transfer area between components, is the temperature gradient along the heat transfer path, ΔT is the temperature difference between two nodes, and R th,cond is the thermal resistance parameter.

[0081] Furthermore, when the sodium-ion battery in the sodium-ion energy storage system is exposed to air, the temperature difference between its surface and the air will cause heat to dissipate by convection. That is to say, when heat is transferred by convection, the formula for obtaining the surface heat dissipation energy is specifically:

[0082] Q conv = hA h (T i - T ∞ )

[0083] where: Q conv is the surface heat dissipation energy, h is the convective heat transfer coefficient, A h is the heat dissipation area, T i is the surface temperature of the i-th node, T ∞ is the fluid temperature far from the object surface.

[0084] Furthermore, in a high-temperature environment, especially when the surface temperature of the sodium-ion battery is much higher than the ambient temperature, there is also radiative heat transfer. That is to say, when heat is transferred by radiation, the formula for obtaining the radiative heat transfer energy is specifically:

[0085]

[0086] where: Q rad is the radiative heat transfer energy, ε is the emissivity, σ is the Stefan-Boltzmann constant, A r is the radiation area, T i 4 is the absolute temperature of the i-th node surface, is the absolute temperature of the j-th node surface.

[0087] In the process of specific implementation, the standard 18650-type sodium-ion battery is the i-th node, its mass is 0.0465 kg, and the specific heat capacity is 837 J / (kg·K), then the heat capacity parameter C th，i corresponding to the i-th node is 38.9 J / K. At the same time, the internal heat generation power of the 18650-type sodium-ion battery during charging and discharging is 1 W, and 0.5 W of heat flows in and 0.8 W of heat flows out. Then the rate of change of temperature with time at the i-th node is 0.018 K / s. That is to say, the temperature at the 18650-type sodium-ion battery increases at a rate of 0.018 K per second.

[0088] Step S1.4: Determine the thermal risk area. That is, through the thermal management model constructed in step S1.3, obtain the temperature distribution data corresponding to the single battery at each node, and at the same time, according to the rate of change of the temperature with time at the i-th node obtained in step S1.3 Obtain the thermal risk score corresponding to each node area, and determine the thermal risk area through the thermal risk score. Specifically as follows:

[0089] Step W1: Determine the safe temperature threshold range. That is, according to the type of sodium-ion battery, determine its safe operating temperature range, and based on this safe operating temperature range, determine the safe temperature threshold range. In the process of specific implementation, the safe operating range of the sodium-ion battery is -20°C to 60°C. That is, taking this safe operating range as the standard, the safe temperature threshold in this embodiment is -18°C to 58°C. That is to say, the lower limit temperature of this safe temperature threshold is -18°C, and the upper limit temperature is 58°C.

[0090] Step W2: Determine the preliminary thermal risk area. That is, obtain the real-time operating temperature of the single battery at each node, and compare this real-time operating temperature with the safe temperature threshold range determined in step W1, and based on the comparison result, determine the preliminary thermal risk area from it. Specifically:

[0091] When the obtained real-time operating temperature is not within the safe temperature threshold range, the single battery area at the node corresponding to this real-time operating temperature is the preliminary thermal risk area, otherwise it is not the preliminary thermal risk area.

[0092] Step W3: Obtain the thermal risk score. That is, according to the real-time operating temperature obtained in step W2 and the rate of change of the temperature with time at the i-th node obtained in step S1.3 Obtain the thermal risk score corresponding to each node. Specifically:

[0093]

[0094] Where: S i is the thermal risk score corresponding to the i-th node, ω 1 is the temperature weight coefficient, T i is the surface temperature of the i-th node, T avg is the average temperature of the i-th node, ω 2 is the rate of change weight coefficient, is the rate of change of the temperature with time at the i-th node.

[0095] Step W4: Determine the final thermal risk area. That is, according to the thermal risk score obtained in step W3, determine the final thermal risk area from the preliminary thermal risk area determined in step W2. Specifically:

[0096] When the obtained thermal risk score is not less than the preset score threshold, the single-cell battery area at the node corresponding to the thermal risk score is the final thermal risk area; otherwise, it is not the final thermal risk area.

[0097] It should be noted that during the process of setting the preset score threshold in this embodiment, its specific value is not fixed and can be specifically set according to the specific requirements during actual use. Therefore, in this embodiment, no specific elaboration will be provided.

[0098] During the specific implementation process, based on the safe operating temperature range provided by the manufacturer (such as the upper safety limit of 60°C) and the analysis of historical operation data, the upper safety limit of the thermal risk score is 15, so the score threshold is set to 12. That is to say, when the obtained thermal risk score is not less than 12, the single-cell battery areas at the nodes corresponding to the thermal risk scores not less than 12 are all the final thermal risk areas; otherwise, they are not the final thermal risk areas.

[0099] Step S2: Obtain warning information. That is, adjust the thermal risk score obtained in step W3 based on the key state parameters of each single-cell battery obtained in step S1.1, and generate corresponding warning information for the determined final thermal risk area according to the adjusted thermal risk score. Specifically as follows:

[0100] Step S2.1: Adjust the weight coefficient. That is, set the initial weight coefficient according to the safe operating temperature range provided by the manufacturer and the historical operation data of the sodium-ion battery pack, and at the same time adjust the initial weight coefficient through an adaptive algorithm. Specifically as follows:

[0101] Step N1: Set the initial weight coefficient. That is, set the initial weight coefficient according to the safe operating temperature range provided by the manufacturer and the historical operation data of the sodium-ion battery pack. During the specific implementation process, set the warning value of the temperature rise rate to 0.5°C / min, and the upper safety limit of the temperature to 58°C. At the same time, the initial temperature weight coefficient is 0.8, and the initial change rate weight coefficient is 0.2.

[0102] Step N2: Determine the weight adjustment range. That is, determine the adjustment range of the weight coefficient according to the initial weight coefficient determined in step N1 to avoid over-adjustment resulting in an over-reactive system. During the specific implementation process, the adjustment range of the temperature weight coefficient ω 1 is 30% of the initial temperature weight coefficient, and the adjustment range of the change rate weight coefficient ω 2 is 30% of the initial change rate weight coefficient.

[0103] Step N3: Determine the weight coefficient to be adjusted. That is, by obtaining the temperature rise rate and temperature through real-time monitoring, compare them with the warning value of the temperature rise rate and the safety upper limit of the temperature set in Step N1, and determine the weight coefficient to be adjusted according to the comparison results. Specifically:

[0104] When the temperature rise rate obtained through real-time monitoring is greater than the warning value of the temperature rise rate, it indicates that the change rate weight coefficient ω 2 needs to be adjusted, that is, increase the change rate weight coefficient ω 2 . Similarly, when the temperature obtained through real-time monitoring is greater than the safety upper limit of the temperature, it indicates that the temperature weight coefficient ω 1 needs to be adjusted, that is, increase the temperature weight coefficient ω 1 . Otherwise, the temperature weight coefficient ω 1 and the change rate weight coefficient ω 2 do not need to be changed.

[0105] It should be noted that when the temperature rise rate obtained through real-time monitoring is greater than the warning value of the temperature rise rate and the temperature obtained through real-time monitoring is greater than the safety upper limit of the temperature, then adjust the temperature weight coefficient ω 1 to reduce the impact of temperature on the battery.

[0106] Step N4: Determine the final weight coefficient. That is, according to the weight coefficient to be adjusted determined in Step N3, obtain the final weight coefficient within the weight adjustment range determined in Step N2. Specifically, according to the specifically preset growth rate, increase the magnitude of the weight coefficient. It should be noted that the magnitude of this weight coefficient always remains within the weight adjustment range determined in Step N2.

[0107] In this embodiment, the preset growth rate is specifically set according to the specific requirements during actual use, so in this embodiment, no specific elaboration will be made. During the specific implementation process, for the first node, the temperature rates at three consecutive time points within a certain period of time are 0.7 °C / min, 0.8 °C / min, and 0.9 °C / min respectively, and all are greater than the warning value of the temperature rise rate of 0.5 °C / min. Then at this time, increase the change rate weight coefficient ω 2 , that is, increase the change rate weight coefficient ω 2 from 0.2 to 0.3.

[0108] Similarly, during the specific implementation process, the temperature of the sixth node continuously exceeds the safety upper limit of the temperature of 58 °C, and the current temperature is 58.5 °C. Then at this time, increase the temperature weight coefficient ω 1 , that is, increase the temperature weight coefficient ω 1 from 0.8 to 0.9.

[0109] Step S2.2: Obtain the real-time heat risk area. That is, according to the weight coefficients determined in Step S2.1, update the heat risk scoring formula to obtain the real-time heat risk area. That is, according to the adjusted heat risk scoring formula, repeat Step W3 and Step W4 to obtain the real-time heat risk area.

[0110] Step S2.3: Generate warning information. That is, according to the heat risk score corresponding to each real-time heat risk area, compare it with the warning score to determine its corresponding alarm level. In this embodiment, there are three alarm levels, specifically:

[0111] Level 1 alarm (warning score is 20 - 30), at this time, an alarm prompt is issued to remind the operator to pay attention.

[0112] Level 2 alarm (warning score is 30 - 40), at this time, the automatic cooling measure is triggered to reduce the temperature of the single battery.

[0113] Level 3 alarm (warning score is greater than 40), at this time, the emergency response measure is started, such as cutting off the power supply or activating the emergency cooling system.

[0114] During the specific implementation process, the current temperature of the single battery at the 7th node is 58°C, the average temperature is 25°C, the temperature change rate over time is 0.9°C / min, and at the same time, the temperature weight coefficient ω 1 is 0.9, and the change rate weight coefficient ω 2 is 0.3. Therefore, the heat risk score in this embodiment is 29.97. That is to say, the single battery at the 7th node is in the Level 1 alarm state. That is, if the single battery is in the Level 1 alarm state, the operator needs to monitor and pay attention to this single battery.

[0115] Step S3: Generate adjustment strategies. That is, according to the final heat risk area determined in Step W4 and the warning information generated in Step S2.3, adjust the charge and discharge rates and load distribution of each single battery to ensure that the operation of the single battery is always in the optimal working range. Specifically as follows:

[0116] Step S3.1: Reduce the charge and discharge rate. That is, according to the warning information generated in Step S2.3, reduce the charge and discharge rate of this single battery. Specifically, according to the best charge and discharge rate provided by the manufacturer (C / 10 - C / 2, where C is the rated capacity of the sodium-ion battery), determine the best charge and discharge rate range of the sodium-ion battery in this embodiment as C / 10 - C / 2. During the specific implementation process, when the rated capacity of the sodium-ion battery is 1000 mAh, the minimum charge and discharge current is 100 mA, and the maximum charge and discharge current is 500 mA.

[0117] Furthermore, when the warning information generated in step S2.3 is a level-1 alarm, the optimal charge-discharge rate range can be reduced from C / 2 to C / 5. When the warning information generated in step S2.3 is a level-2 alarm, the optimal charge-discharge rate range can be further reduced to C / 10.

[0118] Step S3.2: Determine the load distribution. That is, based on the final thermal risk area determined in step W4, identify the healthy single-cell batteries that are not in the final thermal risk area and the sub-healthy single-cell batteries in the final thermal risk area. At the same time, reduce the load on the sub-healthy single-cell batteries and increase the load on the healthy single-cell batteries.

[0119] During the specific implementation process, for a sodium-ion battery pack composed of multiple single-cell batteries, the state parameters of the first single-cell battery are: current temperature 58°C, average temperature 25°C, temperature change rate over time 0.9°C / min, temperature weight coefficient 0.9, and change rate weight coefficient 0.3. Therefore, the thermal risk score of the first single-cell battery is 29.97, which is a level-1 alarm. Thus, the charge-discharge rate of the first single-cell battery is reduced to C / 5.

[0120] Furthermore, in the above-mentioned sodium-ion battery pack composed of multiple single-cell batteries, the state parameters of the ninth single-cell battery are: current temperature 28°C, average temperature 25°C, temperature change rate over time 0.2°C / min, temperature weight coefficient 0.9, and change rate weight coefficient 0.3. Therefore, the thermal risk score of the ninth single-cell battery is 2.76. Thus, the load on the ninth single-cell battery is increased.

[0121] This embodiment also provides a dynamic load management system for a sodium-ion energy storage system based on an intelligent algorithm. This dynamic load management system for a sodium-ion energy storage system uses a dynamic load management method for a sodium-ion energy storage system based on an intelligent algorithm in the above-mentioned embodiment.

[0122] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. A dynamic load management method for a sodium ion energy storage system based on an intelligent algorithm, characterized in that: Included are: Determine the thermal risk area: determine the preliminary thermal risk area through the thermal management model, obtain the thermal risk score of each single battery, and determine the final thermal risk area from the preliminary thermal risk area; Obtaining early warning information: adjusting the thermal risk score according to the state parameters of each single battery, and generating early warning information according to the adjusted thermal risk score; Generate an adjustment strategy: According to the final thermal risk area and warning information, adjust the charge and discharge rate and load distribution of each single battery, including: Reducing the charge and discharge rate: according to the warning information, reducing the charge and discharge rate of the single cell within the optimal charge and discharge rate range of the sodium ion battery; Determine load distribution: determine healthy cells and sub-healthy cells according to the final thermal risk area, and reduce the load of the sub-healthy cells and increase the load of the healthy cells.

2. According to claim 1, a method for dynamic load management of a sodium ion energy storage system based on an intelligent algorithm is characterized in that: The process of establishing the thermal management model includes: Set up a sensor network: In the sodium-ion battery pack, set up a sensor network to obtain the state parameters of each single cell; Setting thermal parameters: Obtaining thermal capacity parameters, thermal resistance parameters and contact thermal resistance parameters through the state parameters of the single battery, specifically: Where: C th is the heat capacity parameter, m is the weight of the single battery and its components, c p is the specific heat capacity of the single cell and its components, R th，cond is the thermal resistance parameter, L c is the heat transfer distance between components, k c is the thermal conductivity between the components, A c is the heat transfer area between components, R th，contact is the contact thermal resistance parameter, ΔT is the temperature difference between the two contact surfaces, q is the heat flow per unit time, A ct is the contact area between different materials, k ct is the thermal conductivity of the contact material, L ct is the effective thickness of the contact layer; Constructing a thermal management model: Obtaining the energy conservation equation corresponding to the single cell at each node through the heat capacity parameter, thermal resistance parameter and contact thermal resistance parameter, specifically: Where: T i is the surface temperature of the ith node, P i is the power generated inside the i-th node, Q in，i is the heat inflow corresponding to the i-th node, Q out，i is the outflow heat corresponding to the i-th node, C th，i is the heat capacity parameter corresponding to the i-th node, t is the time interval, is the rate of change of temperature at the i-th node with time.

3. According to claim 2, a method for dynamic load management of a sodium ion energy storage system based on an intelligent algorithm is characterized in that: The heat transfer mechanisms of single cells during operation include conduction, convection and radiation, specifically: Where: Q cond is the heat transfer rate, k c is the thermal conductivity between the components, A c is the heat transfer area between the components, is the temperature gradient along the heat transfer path, ΔT is the temperature difference between two nodes, R th，cond is the thermal resistance parameter, Q conv is the surface heat dissipation energy, h is the convection heat transfer coefficient, A h is the heat dissipation area, T i is the surface temperature of the ith node, T ∞ is the temperature of the fluid away from the surface of the object, Q rad is the radiation heat transfer energy, ε is the emissivity, σ is the Stefan-Boltzmann constant, A r is the radiation area, is the absolute temperature of the surface of the ith node, is the absolute temperature of the surface of the jth node.

4. According to a method for dynamic load management of a sodium ion energy storage system based on an intelligent algorithm according to claim 1, it is characterized in that: Determine the final thermal risk areas, including: W1: Determine the safe temperature threshold range: Determine the safe temperature threshold range based on the safe operating temperature range of the sodium ion battery; W2: Determine the preliminary thermal risk area: compare the real-time operating temperature of the single battery at each node with the safety temperature threshold range, and determine the preliminary thermal risk area based on the comparison result, specifically: When the real-time operating temperature is not within the safe temperature threshold range, the single cell area at the node corresponding to the real-time operating temperature is the preliminary thermal risk area, otherwise it is not the preliminary thermal risk area; W3: Obtaining thermal risk score: According to the real-time operating temperature and the rate of change of temperature over time, obtain the thermal risk score corresponding to the single cell at each node, specifically: Where: S i is the thermal risk score corresponding to the i-th node, ω1 is the weight coefficient corresponding to the temperature, T i is the surface temperature of the ith node, T avg is the average temperature of the i-th node, ω2 is the weight coefficient corresponding to the rate of change, is the rate of change of temperature at the i-th node with time; W4: Determine the final thermal risk area: Compare the thermal risk score corresponding to each single cell at each node with the score threshold, and determine the final thermal risk area from the preliminary thermal risk area according to the comparison result, specifically: When the thermal risk score is not less than the score threshold, the single cell area at the node corresponding to the thermal risk score is the final thermal risk area, otherwise it is not the final thermal risk area.

5. According to claim 1, a method for dynamic load management of a sodium ion energy storage system based on an intelligent algorithm is characterized in that: Generate early warning information, including: Adjust weight coefficient: set the initial weight coefficient and adjust it through adaptive algorithm; Obtain real-time thermal risk areas: Update the thermal risk scoring formula through the adjusted weight coefficients to obtain real-time thermal risk areas; Generate warning information: According to the thermal risk score corresponding to each of the real-time thermal risk areas, compare the thermal risk score with the warning score, and determine the alarm level corresponding to each of the real-time thermal risk areas according to the comparison result, specifically: When the thermal risk score is between 20 and 30, it is a level 1 alarm, and an alarm prompt is issued; When the thermal risk score is between 30 and 40, it is a level 2 alarm, triggering automatic cooling measures; When the thermal risk score is greater than 40, it is a level 3 alarm, and emergency response measures are initiated.

6. A method for dynamic load management of a sodium ion energy storage system based on an intelligent algorithm according to claim 5, characterized in that: Adjust the initial weight coefficients, including: N1: Set the initial weight coefficient: Set the initial weight coefficient according to the safe working range and operating data; N2: Determine the weight adjustment range: Determine the adjustment range of the weight coefficient according to the initial weight coefficient; N3: Determine the weight coefficient to be adjusted: Compare the temperature rise rate and temperature obtained in real time with the warning value of the temperature rise rate and the safety upper limit of the temperature, and determine the weight coefficient to be adjusted based on the comparison result, specifically: When the temperature rise rate obtained in real time is greater than the temperature rise rate warning value, the change rate weight coefficient is increased, otherwise the change rate weight coefficient remains unchanged; When the temperature obtained in real time is greater than the safe upper limit of the temperature, the temperature weight coefficient is increased, otherwise the temperature weight coefficient remains unchanged; N4: Determine the final weight coefficient: within the weight adjustment range, increase the weight coefficient to be adjusted according to a preset growth rate.

7. A method for dynamic load management of a sodium ion energy storage system based on an intelligent algorithm according to claim 6, characterized in that: When the temperature rise rate obtained in real time is greater than the warning value of the temperature rise rate, and the temperature obtained in real time is greater than the safety upper limit of the temperature, the temperature weight coefficient is increased.

8. A dynamic load management system for a sodium ion energy storage system based on an intelligent algorithm, characterized in that: The sodium ion energy storage system dynamic load management system uses a sodium ion energy storage system dynamic load management method based on an intelligent algorithm as described in any one of claims 1-6.

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