Thermal field induction-based safety management method and system for centralized charging of electric bicycles

Abstract

The application provides a kind of electric bicycle centralized charging safety management method and system based on thermal field induction, it is related to electric bicycle charging safety technical field, the application is by collecting the surface temperature sequence of multiple charging nodes in centralized parking charging area, the leakage current sequence and loop impedance sequence of the power supply loop corresponding to each charging node are synchronously collected;Calculate the temperature gradient change rate to determine the potential risk node;Extract the inflation starting time and short circuit starting time and calculate the delay duration;The leakage current change rate is compensated in advance using the delay duration to obtain the insulation state change curve and extrapolate the estimated short circuit time;When the estimated short circuit time approaches, issue a power-off instruction;According to the valve opening control signal generated by the arc energy change rate to control the release of fire extinguishing medium by fire fighting equipment, early warning and active intervention can be realized in the early stage of battery thermal runaway.

Description

Technical Field

[0001] This application relates to the field of electric bicycle charging safety technology, and in particular to a method and system for centralized charging safety management of electric bicycles based on thermal field induction. Background Technology

[0002] With the continuous growth in the number of electric bicycles, the safety management of centralized parking and charging areas has become increasingly prominent. During charging, electric bicycle batteries may experience thermal runaway due to internal short circuits, overcharging, or external damage, potentially leading to fires or even explosions. Because centralized charging areas typically have densely packed vehicles, a fire at one charging point can easily spread to surrounding vehicles, causing severe property damage and personal injury.

[0003] Existing electric bicycle charging safety management solutions mainly rely on passive fire-fighting equipment such as smoke detectors and temperature detectors. These devices only trigger alarms after a fire has already occurred and produced obvious smoke or high temperatures, resulting in a delayed response and making it difficult to provide early warning and intervention in the early stages of battery thermal runaway. In addition, while traditional overcurrent protection devices and leakage protection devices can cut off the power supply circuit when electrical parameters reach protection thresholds, their protective actions are usually based on fixed threshold judgments and lack the ability to predict changes in the battery's internal state, thus failing to achieve early intervention.

[0004] More importantly, during battery thermal runaway, the internal temperature rise leading to electrolyte decomposition and gas expansion typically precedes significant changes in external electrical parameters. In other words, abnormal increases in battery surface temperature often precede drastic changes in leakage current and loop impedance. However, existing technologies fail to fully utilize the time difference between temperature changes and electrical parameter changes to build predictive models, thus lacking in the timeliness and accuracy of early warnings. Furthermore, existing solutions lack a coordinated linkage mechanism between power outage protection and fire response; the arc energy generated after a power outage is not effectively utilized to guide the precise control of fire-fighting equipment, making it difficult to accurately match the release quantity and timing of extinguishing agents with the actual emergency situation. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for centralized charging safety management of electric bicycles based on thermal field sensing, in order to solve the problems in the existing technology that the safety early warning is lagging behind the development of danger in centralized charging scenarios of electric bicycles, the lack of the ability to predict the trend of battery thermal runaway process, and the lack of coordination between power outage protection and fire response, which leads to the early warning and handling of the safety management system lagging behind the development of danger.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a method for centralized charging safety management of electric bicycles based on thermal field induction, the method comprising: Collecting surface temperature sequences of a plurality of charging nodes in a centralized parking and charging area of electric bicycles, synchronously collecting leakage current sequences and loop impedance sequences of power supply loops corresponding to the charging nodes; Calculating a temperature gradient change rate of the surface temperature sequences, and determining a charging node with a temperature gradient change rate exceeding a first preset threshold as a potential risk node; Extracting an inflation start time and a short circuit start time from the surface temperature sequences and the loop impedance sequences of the potential risk node respectively, and calculating a delay duration between the short circuit start time and the inflation start time; Compensating a change rate of the leakage current sequence of the potential risk node in advance by using the delay duration to obtain an insulation state change curve, and extrapolating the insulation state change curve to obtain an estimated short circuit time; When a difference between the estimated short circuit time and a current time is less than a preset safety advance, issuing a power-off instruction to a protection switch on the power supply loop corresponding to the potential risk node; Generating a valve opening degree control signal according to a change rate of arc energy over time generated when the protection switch executes the power-off instruction, and controlling a fire extinguishing device to release fire extinguishing medium.

[0007] Optionally, compensating the change rate of the leakage current sequence of the potential risk node in advance by using the delay duration to obtain the insulation state change curve, comprises: Extracting a battery shell thickness corresponding to the potential risk node from a pre-established device parameter library, and constructing a thermal resistance coefficient combined with the battery shell thickness and the delay duration; Calculating an initial change rate of the leakage current sequence in a current time window; Multiplying the initial change rate and the thermal resistance coefficient to obtain a slope compensation value, and adding the initial change rate and the slope compensation value to obtain a target change rate; Extending a trajectory of the leakage current sequence in the current time window according to the target change rate to obtain the insulation state change curve.

[0008] Optionally, extending the trajectory of the leakage current sequence in the current time window according to the target change rate to obtain the insulation state change curve, comprises: Extracting a cutoff time and a cutoff leakage current corresponding to a last collection point in the current time window of the leakage current sequence; Determining a prediction time step, and taking an addition result of the prediction time step and the cutoff time as a prediction time node; The target rate of change is multiplied by the predicted time step to obtain the leakage current increment, and the result of adding the leakage current increment to the cutoff leakage current is used as the predicted leakage current value corresponding to the predicted time node. Plot the cutoff leakage current and the predicted leakage current value of all time windows in chronological order and connect them to generate an insulation state change curve.

[0009] Optionally, the expansion initiation time and short-circuit initiation time are extracted from the surface temperature sequence and the loop impedance sequence of the potential risk node, respectively, including: Calculate the difference in temperature between adjacent temperature values ​​in the surface temperature sequence of the potential risk node; Find the first data collection time point after the change difference reaches a local maximum value, and take it as the expansion start time; Identify multiple time points in the loop impedance sequence of the potential risk node where the resistance value continuously decreases, and calculate the impedance decrease per unit time corresponding to the multiple time points. When the impedance drop exceeds a preset impedance threshold, the first time point among the plurality of time points is taken as the short circuit start time.

[0010] Optionally, the estimated short-circuit time is obtained by extrapolation based on the insulation state change curve, including: Extract the breakdown current corresponding to the potential risk node from a pre-established equipment parameter library; The predicted current value is determined according to the target rate of change corresponding to the insulation state change curve, and the target time node when the predicted current value reaches the breakdown current is determined. The target time point is used as the estimated short-circuit moment representing the complete failure of battery insulation.

[0011] Optionally, before generating the valve opening control signal based on the rate of change of arc energy over time generated when the protective switch executes a power-off command, the method further includes: According to the preset sampling period, the transient voltage data and transient current data generated when the protection switch executes the power-off command are continuously collected; Extract the effective voltage value and effective current value within each sampling period from the transient voltage data and the transient current data; Multiply the effective voltage value, the effective current value, and the duration of the sampling period to obtain the arc energy corresponding to each sampling period; Calculate the energy difference of the arc energy corresponding to adjacent sampling periods; Dividing the energy difference by the duration of the sampling period yields the rate of change of the arc energy over time.

[0012] Optionally, a valve opening control signal is generated based on the rate of change of arc energy over time generated when the protection switch executes a power-off command, including: Based on a pre-established mapping table between energy change rate and valve opening, the target valve opening value corresponding to the change rate of arc energy over time is determined. The target valve opening value is converted into a valve opening control signal and sent to the fire-fighting equipment.

[0013] Optionally, calculating the rate of change of the temperature gradient of the surface temperature sequence includes: The temperature difference between each charging node at adjacent data collection time points is calculated based on the surface temperature sequence. Divide the temperature difference by the time interval between adjacent data collection points to obtain the initial temperature change rate. Extract the temperature fluctuation amplitude of the surface temperature sequence within a preset time window; The initial temperature change rate is smoothed and filtered using the temperature fluctuation amplitude to obtain the temperature gradient change rate.

[0014] Optionally, controlling the release of extinguishing agents by the fire-fighting equipment includes: Extract the physical location coordinates of the potential risk nodes within the centralized parking and charging area; Determine the target fire-fighting equipment closest to the potential risk node based on the physical location coordinates; Send the valve opening control signal to the target fire-fighting equipment; The target fire-fighting equipment is controlled to release the extinguishing medium toward the physical location coordinates according to the opening degree indicated by the valve opening control signal.

[0015] Secondly, this application provides a centralized charging safety management system for electric bicycles based on thermal field sensing, the system comprising: The acquisition module is used to acquire the surface temperature sequence of multiple charging nodes in the centralized parking and charging area of ​​electric bicycles, and simultaneously acquire the leakage current sequence and circuit impedance sequence of the power supply circuit corresponding to each charging node. The determination module is used to calculate the temperature gradient change rate of the surface temperature sequence and determine the charging node whose temperature gradient change rate exceeds a first preset threshold as a potential risk node. The calculation module is used to extract the expansion start time and short-circuit start time from the surface temperature sequence and the loop impedance sequence of the potential risk node, respectively, and calculate the delay time between the short-circuit start time and the expansion start time; The compensation module is used to perform advance compensation on the rate of change of the leakage current sequence of the potential risk node using the delay duration, so as to obtain the insulation state change curve, and extrapolate the insulation state change curve to obtain the estimated short circuit time. The sending module is used to send a power-off command to the protection switch on the power supply circuit corresponding to the potential risk node when the difference between the estimated short-circuit time and the current time is less than the preset safety lead time. The generation module is used to generate a valve opening control signal based on the rate of change of the arc energy generated when the protection switch executes the power-off command over time, thereby controlling the fire-fighting equipment to release the extinguishing medium.

[0016] The electric bicycle centralized charging safety management method based on thermal field induction provided in this application has the following beneficial effects: By collecting three types of data—surface temperature sequence, leakage current sequence, and loop impedance sequence—at charging nodes, this application achieves multi-dimensional monitoring of battery charging status; by calculating the temperature gradient change rate, this application can identify potential risk nodes in the early stages of battery thermal runaway; by extracting the delay time between the expansion initiation time and the short circuit initiation time and constructing a dimensionless thermal resistance coefficient, this application uses this delay time to pre-compensate the leakage current change rate, effectively making up for the time difference between temperature change and electrical parameter change, and improving the accuracy and timeliness of short circuit prediction; by establishing a mapping relationship between the arc energy change rate and the valve opening of fire-fighting equipment, this application achieves precise coordinated linkage between power outage protection and fire response, making the release amount of extinguishing medium accurately match the actual danger. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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 based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a centralized charging safety management method for electric bicycles based on thermal field induction, provided for an embodiment of this application; Figure 2 A schematic diagram illustrating the calculation of temperature gradient change rate and the determination of potential risk nodes provided in this application embodiment; Figure 3 This application provides a schematic diagram for extracting the expansion start time and short circuit start time in an embodiment of the present application. Figure 4 A schematic diagram illustrating the calculation of the sliding time window and leakage current change rate is provided for an embodiment of this application. Figure 5 An insulation state change curve and a schematic diagram of the estimated short-circuit moment are provided for embodiments of this application; Figure 6 A schematic diagram of an arc energy calculation and fire control process provided in this application embodiment; Figure 7 This is a schematic diagram of a centralized charging safety management system for electric bicycles based on thermal field induction, provided as an embodiment of this application. Detailed Implementation

[0019] During the charging process of electric bicycle batteries, various abnormal reactions may occur inside the battery due to various reasons, leading to a rapid rise in internal temperature and eventually thermal runaway. In the evolution of thermal runaway, the increase in internal battery temperature and gas expansion usually precede changes in externally observable electrical parameters. This means that if the safety management system relies solely on changes in leakage current or loop impedance to determine the battery status, the system's warning action will have a certain time lag. To overcome this technical problem, this application proposes a centralized charging safety management method for electric bicycles based on thermal field sensing. This method comprehensively utilizes temperature field information and electrical parameter information, and uses the time difference between temperature changes and electrical changes to proactively compensate for the rate of change of leakage current. This allows the safety management system to predict the battery short-circuit moment in advance and implement proactive intervention.

[0020] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The core of this application is to provide a centralized charging safety management method for electric bicycles based on thermal field induction. A flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes: S101. Collect the surface temperature sequence of multiple charging nodes in the centralized parking and charging area of ​​electric bicycles, and simultaneously collect the leakage current sequence and circuit impedance sequence of the power supply circuit corresponding to each charging node.

[0022] In this embodiment, the centralized parking and charging area refers to a centralized charging place for electric bicycles specially set up in places such as residential communities, commercial buildings or public parking lots. There are multiple charging positions in this area, and each charging position corresponds to a charging node. The charging node refers to the monitoring unit consisting of the electric bicycle being charged at each charging position and its corresponding power supply circuit.

[0023] likeFigure 2 As shown, the centralized parking and charging area includes three charging nodes 11: charging node 11a, charging node 11b, and charging node 11c. The safety management system deploys a temperature sensor 12 at each charging node 11. The temperature sensor 12 is attached to the surface of the electric bicycle battery casing and is used to continuously collect temperature data of the battery's outer surface at that charging node 11. The surface temperature sequence refers to the time-ordered sequence of temperature values ​​formed by the temperature sensor continuously collecting temperature values ​​at fixed intervals at each charging node. For example, the safety management system continuously collects temperature values ​​from charging node 11a at 5-second intervals. , , These temperature values, arranged by time, constitute the surface temperature sequence of the charging node 11a. The specific value of the sampling interval can be set according to the safety requirements of the actual application scenario, for example, it can be set to any value between 1 second and 30 seconds. Figure 2 As shown on the right, the temperature sequences of charging node 11a and charging node 11b are provided.

[0024] Simultaneously with temperature acquisition, the safety management system also acquires the leakage current sequence of the power supply circuit corresponding to each charging node 11 through leakage current sensor 13. The leakage current sensor 13 is installed on the power supply circuit and continuously measures the leakage current value at the same acquisition interval as the temperature sensor 12, thereby forming a leakage current sequence. At the same time, the safety management system continuously measures the loop impedance value of the power supply circuit corresponding to each charging node 11 at the same acquisition interval through impedance measurement device 14, thereby forming a loop impedance sequence.

[0025] S102. Calculate the temperature gradient change rate of the surface temperature sequence, and determine the charging node whose temperature gradient change rate exceeds a first preset threshold as a potential risk node.

[0026] In this embodiment, after obtaining the surface temperature sequence of each charging node, the safety management system needs to analyze the temperature change trend of each charging node to screen out potential risk nodes with abnormal temperature rises. Specifically, the process of the safety management system calculating the temperature gradient change rate of the surface temperature sequence includes the following steps: First, the temperature difference between adjacent data acquisition time points for each charging node is calculated based on the surface temperature sequence. Assume a charging node at time... The collected temperature values ​​are At the next moment The collected temperature values ​​are The safety management system then calculates the temperature difference between these adjacent time points. As shown in formula (1): - (1) in, This represents the temperature difference within the k-th sampling interval. and They are time points and Temperature value.

[0027] Next, the temperature difference is divided by the time interval between adjacent sampling points to obtain the initial temperature change rate. As shown in formula (2): = (2) in, Let be the initial temperature change rate within the k-th sampling interval. This represents the time interval between adjacent data collection points. For example, if the temperature sensor collects a temperature of 35.2℃ at time 10 seconds and a temperature of 35.8℃ at time 15 seconds, then the temperature difference is... =35.8℃ - 35.2℃ = 0.6℃ =5 seconds, calculate the initial temperature change rate It is 0.12°C per second.

[0028] Then, the temperature fluctuation amplitude of the surface temperature sequence within a preset time window is extracted. Here, the preset time window refers to a fixed time interval used to statistically analyze the temperature fluctuation characteristics, for example, it can be set to 60 seconds; the temperature fluctuation amplitude refers to the difference between the maximum and minimum values ​​of the temperature sequence within the preset time window.

[0029] Finally, the initial temperature change rate is smoothed using the temperature fluctuation amplitude to obtain the temperature gradient change rate. A weighted moving average filtering method can be used for smoothing, and the specific calculation process is as follows: The safety management system sets a filtering window containing N initial temperature change rate data points, including the current time point and N-1 consecutive time points prior to it, where N is the filtering order (e.g., 5). The temperature fluctuation amplitude within the preset time window for each of these N time points is then calculated. , ... Then, the reciprocal of the temperature fluctuation amplitude is used as the original weight for that time point, as shown in formula (3): = (3) in, The normalized weights at time point j. Let be the temperature fluctuation amplitude within the preset time window of the j-th time point, and let the denominator be the sum of the original weights of all N time points. Finally, the safety management system calculates the temperature gradient change rate at the current time point according to formula (4): G= (4) Where G is the rate of change of the temperature gradient at the current time point. The normalized weights at time point j. Let be the initial temperature change rate at time point j. The principle behind the above formula is: the temperature fluctuation range within a preset time window at a certain time point. A large temperature fluctuation indicates strong measurement noise or environmental interference to the temperature sensor during that time period, resulting in low reliability of the initial temperature change rate at that point. Therefore, the safety management system assigns it a small normalization weight. Conversely, a small temperature fluctuation indicates a larger temperature fluctuation. The hourly rate indicates stable temperature changes and high data reliability during that time period, thus the safety management system assigns it a large normalization weight. Let's illustrate the calculation process with a specific numerical example. Assuming the filter order N is 5, the safety management system calculates the initial temperature change rate at the current time point and the previous four time points. and temperature fluctuation range As shown in Table 1: Table 1 Initial temperature change rate R and temperature fluctuation amplitude A

[0030] In the table above, the total original weights S = 0.50 + 1.00 + 2.00 + 1.00 + 0.50 = 5.00. Taking data point 3 as an example, its normalized weights... = =0.40, meaning this data point received the highest weight due to its smallest temperature fluctuation. The safety management system calculates the temperature gradient change rate G at the current time point as: G = 0.10 × 0.08 + 0.20 × 0.10 + 0.40 × 0.11 + 0.20 × 0.09 + 0.10 × 0.12 = 0.008 + 0.020 + 0.044 + 0.018 + 0.012 = 0.102℃ / s. It can be seen that the final temperature gradient change rate G = 0.102℃ / s is closer to the change rate of data point 3, which has a small temperature fluctuation and high data reliability. =0.11℃ / s, which deviates from the rate of change of data points 1 and 5, where the temperature fluctuation range is large.

[0031] After obtaining the temperature gradient change rate of each charging node, the safety management system compares the temperature gradient change rate of each charging node with a first preset threshold. The first preset threshold is a judgment boundary value set based on statistical data of the temperature rise rate of electric bicycle batteries under normal charging conditions; for example, it can be set to 0.5°C per second, and can be adjusted according to the battery type and environmental conditions in the actual deployment scenario. When the temperature gradient change rate of a charging node exceeds the first preset threshold, the safety management system identifies that charging node as a potential risk node and sends it to the subsequent refined analysis process.

[0032] S103. Extract the expansion start time and short circuit start time from the surface temperature sequence and the loop impedance sequence of the potential risk node, respectively, and calculate the delay time between the short circuit start time and the expansion start time.

[0033] In this embodiment, as Figure 3 As shown, after a charging node is identified as a potential risk node, the safety management system needs to further extract two key time points from the surface temperature sequence and loop impedance sequence of that potential risk node: the expansion initiation time and the short-circuit initiation time. During thermal runaway, the increase in internal temperature first causes the electrolyte to decompose and produce gas, causing the battery casing to expand. Subsequently, the insulation layer inside the battery gradually deteriorates, eventually leading to an internal short circuit. Therefore, the expansion initiation time, which reflects the expansion of the casing, is usually earlier than the short-circuit initiation time, which reflects the insulation deterioration. The time difference between the two is the delay time, which characterizes the degree to which the battery casing hinders the conduction of heat from the inside to the outside: the thicker the casing or the lower the thermal conductivity of the material, the slower the heat conduction and the longer the delay time.

[0034] Specifically, the process of extracting the expansion initiation time is as follows: First, calculate the difference in changes between adjacent temperature values ​​in the surface temperature sequence of the potential risk node, assuming the temperature sequence includes... , , , , , Then, the safety management system calculates the sequence of changes in difference. = - , = - , = - And so on. Then, the first acquisition time node after the change difference reaches a local maximum value is found in the change difference sequence, and this is taken as the start time of the expansion.

[0035] It should be noted that, due to each variation difference It is composed of two adjacent collection time points and The temperature value is calculated, therefore each change difference is... Corresponding to time interval to The first time point refers to the next data collection point when the difference in change reaches a local maximum. For example... Figure 3 As shown on the left, the horizontal axis represents the data acquisition time points, and the vertical axis represents the temperature change difference between adjacent data acquisition time points. Assume the safety management system calculates the temperature change difference sequence as 0.1, 0.3, 0.5, 0.8, 0.7, and 0.4℃, where the temperature change difference of 0.8℃ corresponds to... It is determined by time With time The temperature difference between the two values ​​was calculated, and this value is a local maximum. Starting from 0.8℃, the subsequent change in the temperature difference decreases to 0.7℃. Therefore, the initial time of expansion is determined to be... From a physical mechanism perspective, once the internal expansion of the battery reaches a certain level, the deformation of the outer casing leads to an increase in the heat dissipation area, and the rate of temperature rise on the battery surface begins to slow down. Therefore, the point at which the difference in temperature changes from increasing to decreasing corresponds to the moment when the expansion behavior begins to manifest.

[0036] The process of extracting the short-circuit initiation time is as follows: First, identify multiple time points in the loop impedance sequence of potential risk nodes where the resistance value continuously decreases. Here, "continuously decreasing resistance" means that, starting from a certain time point in the loop impedance sequence acquired by the impedance measurement device, the impedance value of several consecutive time points is lower than the impedance value of the previous time point, and the number of consecutively decreasing time points is not less than a preset continuity judgment threshold. For example, the preset continuity judgment threshold can be set to 3, meaning that at least 3 consecutive time points must show a decreasing resistance trend for the safety management system to recognize it as a continuous decrease in resistance.

[0037] Then, for each pair of adjacent time points within the aforementioned continuous decreasing interval, as shown in formula (5), the impedance decrease per unit time is calculated for each pair: (5) in, Let be the impedance decrease at the k-th pair of adjacent time nodes. and These are the impedance values ​​at two different time points, respectively. The sampling interval is defined as follows. Each impedance drop is checked pairwise to see if it exceeds the preset impedance threshold. If the impedance drop of any pair of adjacent time nodes within the continuous impedance drop interval exceeds the preset impedance threshold, it is confirmed that the continuous impedance drop segment has short-circuit characteristics, and the first time node in the continuous impedance drop interval is taken as the short-circuit start time.

[0038] Assuming a preset impedance threshold of 0.5 ohms per second and a sampling interval of 5 seconds, the continuously decreasing data in a certain segment of the loop impedance sequence is shown in Table 2 and... Figure 3 As shown: Table 2. Data on a Continuous Decline

[0039] As shown in the table above, from time... The impedance value began to decrease continuously, and the time point for this continuous decrease was... to There are 5 pairs, all meeting the continuity threshold. The safety management system calculates the impedance drop for each pair: to The impedance drop was 0.40 ohms per second, which did not exceed the threshold. to The impedance drop was 0.60 ohms per second, exceeding the preset impedance threshold of 0.5 ohms per second. Upon detection... to Once the impedance drop at these nodes first exceeds the threshold, it is confirmed that the continuous falling segment has short-circuit characteristics, and the first time node of the continuous falling segment is set. This serves as the initiation time of the short circuit. The reason for tracing the initiation time back to the first time point of the continuous decline is... The time point when the non-impedance drop first exceeds the threshold or This is because the internal insulation degradation of a battery is a gradual process: when microcracks first appear in the insulation layer, the impedance decreases slowly. As the cracks expand, the impedance decrease accelerates. The moment when the impedance begins to decrease continuously corresponds to the starting point of the insulation degradation process. The moment when the impedance decrease exceeds the threshold is only the confirmation point of accelerated degradation. Positioning the short circuit start time at the starting point of continuous decrease can more accurately capture the true start time of insulation degradation, thereby making the subsequent delay time calculation more accurate.

[0040] After obtaining the expansion start time and the short circuit start time respectively, the safety management system calculates the delay time, as shown in formula (6): (6) in, To delay the duration, The start time of the short circuit. This represents the initial expansion time. The delay duration reflects the time interval between the start of internal expansion of the battery and the subsequent continuous decrease in impedance caused by insulation degradation.

[0041] S104. The change rate of the leakage current sequence of the potential risk node is advanced by using the delay time to obtain the insulation state change curve, and the estimated short circuit time is obtained by extrapolation based on the insulation state change curve.

[0042] In this embodiment, because the leakage current change caused by the internal insulation degradation of the battery has a time lag relative to the temperature change, if the safety management system directly predicts the short circuit time based on the current leakage current change rate, the prediction result will be too late. To compensate for this time lag, the safety management system uses the delay time to perform advance compensation for the leakage current change rate, so that the prediction curve can reflect the actual degradation process of the battery insulation state earlier.

[0043] Specifically, the process of advance compensation includes the following steps: First, the battery casing thickness corresponding to potential risk nodes is extracted from a pre-established equipment parameter database. This database is a pre-established database based on the battery models and specifications of electric bicycles corresponding to each charging node in the centralized parking and charging area. It records parameter information such as the casing thickness, casing material type, and reference value of the heat penetration rate corresponding to each battery model.

[0044] The thermal resistance coefficient is constructed by combining the battery casing thickness and the delay time. The specific construction process of the thermal resistance coefficient is as follows: First, based on the battery casing material type, the corresponding thermal penetration rate reference value is retrieved from the equipment parameter library. The thermal penetration rate reference value is an empirical constant, measured in advance and entered into the equipment parameter library through thermal penetration experiments on various battery casing materials. Its dimension is meters per second. The measurement method is as follows: Take a sample of casing material with a known thickness, apply a constant heat source to one side of the sample, and simultaneously place a temperature sensor on the other side of the sample. Record the moment when the temperature sensor detects the temperature starting to rise. Divide the sample thickness by this moment to obtain the thermal penetration rate reference value. Different materials have different thermal penetration rate reference values. The thermal conduction time constant of the casing is then calculated. and thermal resistance coefficient As shown in formulas (7) and (8): = (7) = (8) in, Let be the time constant for heat conduction in the outer casing, in seconds; Here is a reference value for the thermal penetration rate; d is the battery casing thickness corresponding to the potential risk node. The thermal resistance coefficient is dimensionless. The outer casing thermal conduction time constant represents the theoretical time required for heat to penetrate the outer casing of that thickness under experimental conditions. The thermal resistance coefficient is obtained by dividing the delay time by the outer casing thermal conduction time constant. Since both the delay time and the outer casing thermal conduction time constant are in seconds, the thermal resistance coefficient is a dimensionless numerical value and can be directly used in subsequent multiplication and addition operations. The physical meaning of the thermal resistance coefficient is: the multiple of the actually observed delay time relative to the theoretical thermal conduction time constant. The larger this multiple, the higher the degree of thermal conduction resistance of the battery under actual operating conditions, requiring a greater degree of advance compensation for the leakage current change rate.

[0045] Taking a specific numerical example, assuming the battery casing thickness d = 0.002 meters corresponds to a potential risk node, the casing material is aluminum alloy, and the reference value for thermal penetration rate is... =0.001 m / s, then the calculated heat conduction time constant of the outer shell is... =0.002÷0.001=2 seconds, if =6 seconds, then the thermal resistance coefficient is calculated. =6÷2=3, which is a dimensionless pure numerical value.

[0046] Next, calculate the initial rate of change of the leakage current sequence within the current time window. For example... Figure 4 As shown, this embodiment uses a sliding time window method to segment the leakage current sequence. The sliding time window refers to the analysis interval formed by the safety management system sliding sequentially along the time axis with a fixed window length. There is a certain overlap between adjacent windows. For example, if the window length is set to 60 seconds and the sliding step size is set to 30 seconds, then the first window... The second window covers data from 0 to 60 seconds. The third window covers data from 30 to 90 seconds. The data covers 60 to 120 seconds of data, and so on. The current time window refers to the time window containing the most recently collected data, i.e., the latest window in time. Assume the current system time is in the fourth window. Within the range, then This refers to the current time window. to The historical time window is used, and its length and sliding step can be set according to the safety requirements of the centralized parking and charging area. Within the current time window, the safety management system uses the least squares method to perform linear regression fitting on the leakage current data points collected by the leakage current sensor within the window. The slope of the resulting regression line is the initial rate of change. The dimension is milliamperes per second. The safety management system calculates the target change rate according to formula (9): (9) in, For the target rate of change, The initial rate of change, This is the thermal resistance coefficient. Because... The value is dimensionless. Dimensions and They are the same, both measured in milliamperes per second, and the units of addition are consistent. =3、 Taking 0.05 milliamperes per second as an example, =0.05×(1+3)=0.20 mA / s. It can be seen that the target rate of change after advance compensation is greater than the initial rate of change, which makes the predicted curve rise faster and thus reach the breakdown current threshold earlier.

[0047] Finally, the leakage current sequence within the current time window is extended according to the target rate of change to obtain the insulation state change curve. As shown in formulas (10) and (11), the leakage current sequence is extended: (10) (11) in, This represents the cutoff leakage current of the last data collection point within the current time window. For the corresponding deadline, To predict the time step, To predict leakage current, For predicting time points.

[0048] It should be noted that the calculation of the initial rate of change and the construction of the target rate of change are not performed only once within the current time window, but rather independently by the security management system each time a time window becomes the current window. In other words, when the window... When it is the current window, the security management system calculates... Initial rate of change within and construct The corresponding target rate of change; when the window When it becomes the current window, the security management system calculates... Initial rate of change within and construct The corresponding target rate of change; and so on. Therefore, each time window generates a set of data: the cutoff time of the window, the cutoff leakage current, and the predicted leakage current value calculated based on the target rate of change for that window.

[0049] Extract the cutoff time and cutoff leakage current corresponding to the last acquisition point in the current time window of the leakage current sequence; determine the prediction time step, and take the sum of the prediction time step and the cutoff time as the prediction time node; multiply the target rate of change by the prediction time step to obtain the leakage current increment, and take the sum of the leakage current increment and the cutoff leakage current as the prediction leakage current value corresponding to the prediction time node.

[0050] The prediction time step is determined as follows: the current acquisition interval is used as the baseline step size, and adaptive adjustment is made according to the magnitude of the current initial rate of change. When the initial rate of change is greater than the preset rate of change threshold, the prediction time step is shortened to half of the baseline step size; when the initial rate of change is not greater than the preset rate of change threshold, the prediction time step is set to twice the baseline step size. For example, the preset rate of change threshold can be set to 0.1 mA per second.

[0051] After completing the above calculations in all historical and current time windows, plot the cutoff leakage current and predicted leakage current values ​​of all time windows in chronological order and connect them to generate an insulation state change curve. Each time window contributes two data points: one is the cutoff leakage current at the cutoff time, corresponding to the actual measured value; the other is the predicted leakage current value at the predicted time node, corresponding to the forward predicted value.

[0052] Assuming a sampling interval of 5 seconds, a window length of 60 seconds, a sliding step size of 30 seconds, and a thermal resistivity of 3, the calculation results for each window are shown in Table 3: Table 3 Calculation results for each window

[0053] window The initial rate of change is 0.03 mA per second, which is less than the threshold of 0.1 mA per second. Therefore, the prediction step size is twice the baseline step size of 5 seconds, i.e., 10 seconds. The initial rate of change is 0.11 mA per second, which is greater than the threshold value. Therefore, the prediction step size is half of the baseline step size, i.e., 2.5 seconds. The eight cutoff leakage current data points in the table above (60 seconds = 5.0 mA, 90 seconds = 8.5 mA, 120 seconds = 15.0 mA, 150 seconds = 28.0 mA) and the four predicted leakage current data points (70 seconds = 6.2 mA, 100 seconds = 10.9 mA, 122.5 seconds = 16.1 mA, 152.5 seconds = 30.0 mA), totaling 12 points, are plotted in chronological order and connected to generate the following... Figure 5 The insulation state change curve is shown by the combination of solid and dashed lines.

[0054] Furthermore, after obtaining the insulation state change curve, the safety management system extrapolates from this curve to obtain the estimated short-circuit time. Specifically, the safety management system extracts the breakdown current corresponding to potential risk nodes from the equipment parameter database. The breakdown current refers to the critical leakage current value corresponding to the complete failure of the battery insulation layer. This value is determined based on the insulation withstand voltage parameters and laboratory breakdown test data in the technical specifications provided by the battery manufacturer, and is pre-entered into the equipment parameter library. As shown in formula (12), the estimated short-circuit time is calculated: (12) in, To predict the timing of the short circuit, For breakdown current, This is the cutoff leakage current for the current window. This represents the target rate of change for the current window. Assume the breakdown current of a certain type of lithium-ion battery... =300 mA; Based on the target change rate of the current time window, starting from the cutoff leakage current of the current time window, the predicted current value is gradually increased according to the target change rate. The time point corresponding to when the predicted current value first reaches the breakdown current is the estimated short circuit moment. Taking the above values ​​as an example, the current window Cut-off leakage current =28.0 mA, target rate of change =0.80 milliamperes per second, then =150+(300-28.0)÷0.80=150+340=490 seconds.

[0055] S105. When the difference between the estimated short-circuit time and the current time is less than the preset safety lead time, a power-off command is sent to the protection switch on the power supply circuit corresponding to the potential risk node.

[0056] In this embodiment, after obtaining the estimated short-circuit time, the difference between the estimated short-circuit time and the current time is compared with a preset safety lead time to determine whether an immediate power-off protection operation is required. The preset safety lead time refers to the minimum response time that the safety management system needs to reserve before the estimated short-circuit time arrives. This time needs to cover the communication transmission delay of the power-off command from the safety management system to the protective switch, the mechanical action time of the protective switch, and a certain safety margin. The preset safety lead time can be set to, for example, 30 seconds, and can be adjusted according to the response speed of the protective switch and the delay characteristics of the communication link. When the difference between the estimated short-circuit time and the current time is less than the preset safety lead time, the safety management system immediately issues a power-off command to the protective switch on the power supply circuit corresponding to the potential risk node. After receiving the power-off command, the protective switch disconnects the power supply circuit, cutting off the power supply to the charging node and preventing further thermal runaway and fire caused by battery insulation failure.

[0057] S106. Based on the rate of change of arc energy over time generated when the protection switch executes the power-off command, a valve opening control signal is generated to control the fire-fighting equipment to release the extinguishing medium.

[0058] In this embodiment, as Figure 6 As shown, when a protective switch disconnects the power supply circuit by executing a power-off command, an electric arc will be generated between the contacts of the protective switch due to the presence of inductive load and residual current in the circuit. The magnitude and duration of the arc's energy can reflect the actual current level and energy storage status in the circuit at the moment of power failure, and thus indirectly reflect the current danger level of the battery corresponding to that charging node. Therefore, the rate of change of arc energy is used as a basis for guiding the response strength of fire-fighting equipment.

[0059] Specifically, before generating the valve opening control signal, it is necessary to calculate the rate of change of arc energy over time, such as... Figure 6 As shown.

[0060] S601, according to the preset sampling period It continuously collects transient voltage and current data generated when the protection switch executes a power-off command. Since the duration of the electric arc is typically on the order of milliseconds, the sampling period needs to be set to a time interval, for example... It can be set to 0.1 milliseconds.

[0061] S602. Extract the effective voltage value within each sampling period from the transient voltage data and transient current data. and effective current value The effective value refers to the root mean square value of the voltage or current signal within the sampling period.

[0062] S603. Multiply the effective voltage value, the effective current value, and the duration of the sampling period to obtain the arc energy corresponding to each sampling period, as shown in formula (13): (13) in, Let be the arc energy in the kth sampling period. From a dimensional perspective, the effective voltage value is in volts, the effective current value is in amperes, and the sampling period duration is in seconds. The product of these three values ​​is volts multiplied by amperes multiplied by seconds, which equals joules. That is, the arc energy is in joules, and the dimensions are consistent.

[0063] S604. Calculate the energy difference of the arc energy corresponding to adjacent sampling periods.

[0064] S605. Divide the energy difference by the duration of the sampling period to obtain the rate of change of arc energy over time, as shown in formula (14): (14) in, It is the difference in arc power between adjacent sampling periods, that is, the change in arc energy per unit time, and its dimension is joules per second, or watts. The arc energy in the (k+1)th sampling period. Let the arc energy be the value in the kth sampling period. This represents the duration of the sampling period. The larger this value, the greater the increase in energy released by the electric arc per unit time, and the higher the corresponding fire risk level.

[0065] After obtaining the rate of change of arc energy over time, the target valve opening value corresponding to the pre-established mapping table between the rate of change of energy and the valve opening is determined. This mapping table is a lookup table pre-defined according to the fire risk level corresponding to different levels of arc energy change rate, as shown in Table 4. Table 4 Mapping Relationship Table

[0066] The method for determining the target valve opening value according to Table 4 is as follows: compare the calculated arc energy change rate with the change rate intervals in the table above one by one, determine the interval in which the change rate falls, and read the target valve opening value corresponding to that interval. For example, if the arc energy change rate calculated by the safety management system is 80 watts, then this value falls into the 50 to 150 watt interval shown in the second row, and the target valve opening value is determined to be 50%. The design principle of this mapping relationship is: the larger the arc energy change rate, the higher the residual energy in the circuit at the moment of power failure, and the greater the danger of the battery. Therefore, the safety management system needs to control the fire-fighting equipment to release the extinguishing medium with a larger valve opening to match the higher fire-fighting demand.

[0067] The target valve opening value is converted into a valve opening control signal and sent to the fire-fighting equipment. The process of controlling the fire-fighting equipment to release the extinguishing medium specifically includes: extracting the physical location coordinates of the potential risk node within the centralized parking and charging area, which are pre-calibrated and entered into the safety management system during the system deployment phase; selecting the fire-fighting equipment closest to the potential risk node from all deployed fire-fighting equipment based on the physical location coordinates using the Euclidean distance calculation method; sending a valve opening control signal to the target fire-fighting equipment, controlling the target fire-fighting equipment to release the extinguishing medium toward the physical location coordinates according to the opening degree indicated by the valve opening control signal.

[0068] Through the coordinated operation of steps S101 to S106, the safety management system achieves closed-loop management across the entire chain, from data acquisition, risk assessment, trend prediction, proactive power outage, to precise fire protection. The calculation of the temperature gradient change rate and the identification of potential risk nodes provide a foundation for early warning; the extraction of the delay between the expansion initiation time and the short-circuit initiation time provides a basis for proactive compensation; the method of using the dimensionless thermal resistance coefficient to proactively compensate for the leakage current change rate and generate an insulation state change curve effectively compensates for the time difference between temperature changes and electrical changes; and the mapping and linkage between the arc energy change rate and the valve opening of fire-fighting equipment achieves precise coordination between power outage protection and fire response.

[0069] Figure 7 This application provides a schematic diagram of a specific implementation of a centralized charging safety management system for electric bicycles based on thermal field induction, as shown in the following example. Figure 7 The system may include: The acquisition module 71 is used to acquire the surface temperature sequence of multiple charging nodes in the centralized parking and charging area of ​​electric bicycles, and simultaneously acquire the leakage current sequence and circuit impedance sequence of the power supply circuit corresponding to each charging node. The determination module 72 is used to calculate the temperature gradient change rate of the surface temperature sequence and determine the charging node whose temperature gradient change rate exceeds a first preset threshold as a potential risk node. Calculation module 73 is used to extract the expansion start time and short circuit start time from the surface temperature sequence and the loop impedance sequence of the potential risk node, respectively, and calculate the delay time between the short circuit start time and the expansion start time; The compensation module 74 is used to perform advance compensation on the rate of change of the leakage current sequence of the potential risk node using the delay duration, so as to obtain the insulation state change curve, and extrapolate the insulation state change curve to obtain the estimated short circuit time. The sending module 75 is used to send a power-off command to the protection switch on the power supply circuit corresponding to the potential risk node when the difference between the estimated short-circuit time and the current time is less than the preset safety lead time. The generation module 76 is used to generate a valve opening control signal based on the rate of change of the arc energy generated when the protection switch executes the power-off command over time, and to control the fire-fighting equipment to release the extinguishing medium.

[0070] The specific working principles and implementation methods of each module of the security management system in this embodiment correspond to those of the aforementioned method embodiments. For relevant details, please refer to the method section description, which will not be repeated here.

[0071] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0072] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for centralized charging safety management of electric bicycles based on thermal field induction, characterized in that, include: The surface temperature sequence of multiple charging nodes in the centralized parking and charging area of ​​electric bicycles was collected, and the leakage current sequence and circuit impedance sequence of the power supply circuit corresponding to each charging node were collected simultaneously. Calculate the temperature gradient change rate of the surface temperature sequence, and identify charging nodes whose temperature gradient change rate exceeds a first preset threshold as potential risk nodes; The expansion initiation time and short-circuit initiation time are extracted from the surface temperature sequence and the loop impedance sequence of the potential risk node, respectively, and the delay time between the short-circuit initiation time and the expansion initiation time is calculated. The rate of change of the leakage current sequence of the potential risk node is advanced by using the delay duration to obtain the insulation state change curve, and the estimated short circuit time is obtained by extrapolation based on the insulation state change curve. When the difference between the estimated short-circuit time and the current time is less than the preset safety lead time, a power-off command is sent to the protection switch on the power supply circuit corresponding to the potential risk node. Based on the rate of change of the arc energy generated by the protective switch when it executes the power-off command, a valve opening control signal is generated to control the fire-fighting equipment to release the extinguishing medium.

2. The method according to claim 1, characterized in that, The rate of change of the leakage current sequence at the potential risk node is pre-compensated using the aforementioned delay duration to obtain the insulation state change curve, including: Extract the battery casing thickness corresponding to the potential risk node from the pre-established equipment parameter library, and construct the thermal resistance coefficient by combining the battery casing thickness and the delay duration. Calculate the initial rate of change of the leakage current sequence within the current time window; The product of the initial rate of change and the thermal resistance coefficient is used as the slope compensation value, and the sum of the initial rate of change and the slope compensation value is used as the target rate of change. The leakage current sequence within the current time window is traced according to the target rate of change to obtain the insulation state change curve.

3. The method according to claim 2, characterized in that, The leakage current sequence within the current time window is traced according to the target rate of change to obtain an insulation state change curve, including: Extract the cutoff time and cutoff leakage current corresponding to the last acquisition point in the current time window of the leakage current sequence; Determine the prediction time step, and use the sum of the prediction time step and the cutoff time as the prediction time node; The target rate of change is multiplied by the predicted time step to obtain the leakage current increment, and the result of adding the leakage current increment to the cutoff leakage current is used as the predicted leakage current value corresponding to the predicted time node. Plot the cutoff leakage current and the predicted leakage current value of all time windows in chronological order and connect them to generate an insulation state change curve.

4. The method according to claim 1, characterized in that, The expansion initiation time and short-circuit initiation time are extracted from the surface temperature sequence and the loop impedance sequence of the potential risk nodes, respectively, including: Calculate the difference in temperature between adjacent temperature values ​​in the surface temperature sequence of the potential risk node; Find the first data collection time point after the change difference reaches a local maximum value, and take it as the expansion start time; Identify multiple time points in the loop impedance sequence of the potential risk node where the resistance value continuously decreases, and calculate the impedance decrease per unit time corresponding to the multiple time points. When the impedance drop exceeds a preset impedance threshold, the first time point among the plurality of time points is taken as the short circuit start time.

5. The method according to claim 1, characterized in that, The estimated short-circuit time is obtained by extrapolation based on the insulation state change curve, including: Extract the breakdown current corresponding to the potential risk node from a pre-established equipment parameter library; The predicted current value is determined according to the target rate of change corresponding to the insulation state change curve, and the target time node when the predicted current value reaches the breakdown current is determined. The target time point is used as the estimated short-circuit moment representing the complete failure of battery insulation.

6. The method according to claim 1, characterized in that, Before generating the valve opening control signal based on the rate of change of arc energy over time generated when the protection switch executes a power-off command, the method further includes: According to the preset sampling period, the transient voltage data and transient current data generated when the protection switch executes the power-off command are continuously collected; Extract the effective voltage value and effective current value within each sampling period from the transient voltage data and the transient current data; Multiply the effective voltage value, the effective current value, and the duration of the sampling period to obtain the arc energy corresponding to each sampling period; Calculate the energy difference of the arc energy corresponding to adjacent sampling periods; Dividing the energy difference by the duration of the sampling period yields the rate of change of the arc energy over time.

7. The method according to claim 1, characterized in that, Based on the rate of change of arc energy over time generated when the protective switch executes a power-off command, a valve opening control signal is generated, including: Based on a pre-established mapping table between energy change rate and valve opening, the target valve opening value corresponding to the change rate of arc energy over time is determined. The target valve opening value is converted into a valve opening control signal and sent to the fire-fighting equipment.

8. The method according to claim 1, characterized in that, Calculating the rate of change of the temperature gradient of the surface temperature sequence includes: The temperature difference between each charging node at adjacent data collection time points is calculated based on the surface temperature sequence. Divide the temperature difference by the time interval between adjacent data collection points to obtain the initial temperature change rate. Extract the temperature fluctuation amplitude of the surface temperature sequence within a preset time window; The initial temperature change rate is smoothed and filtered using the temperature fluctuation amplitude to obtain the temperature gradient change rate.

9. The method according to claim 1, characterized in that, Controlling the release of extinguishing agents from fire-fighting equipment, including: Extract the physical location coordinates of the potential risk nodes within the centralized parking and charging area; Determine the target fire-fighting equipment closest to the potential risk node based on the physical location coordinates; Send the valve opening control signal to the target fire-fighting equipment; The target fire-fighting equipment is controlled to release the extinguishing medium toward the physical location coordinates according to the opening degree indicated by the valve opening control signal.

10. A centralized charging safety management system for electric bicycles based on thermal field sensing, characterized in that, include: The acquisition module is used to acquire the surface temperature sequence of multiple charging nodes in the centralized parking and charging area of ​​electric bicycles, and simultaneously acquire the leakage current sequence and circuit impedance sequence of the power supply circuit corresponding to each charging node. The determination module is used to calculate the temperature gradient change rate of the surface temperature sequence and determine the charging node whose temperature gradient change rate exceeds a first preset threshold as a potential risk node. The calculation module is used to extract the expansion start time and short-circuit start time from the surface temperature sequence and the loop impedance sequence of the potential risk node, respectively, and calculate the delay time between the short-circuit start time and the expansion start time; The compensation module is used to perform advance compensation on the rate of change of the leakage current sequence of the potential risk node using the delay duration, so as to obtain the insulation state change curve, and extrapolate the insulation state change curve to obtain the estimated short circuit time. The sending module is used to send a power-off command to the protection switch on the power supply circuit corresponding to the potential risk node when the difference between the estimated short-circuit time and the current time is less than the preset safety lead time. The generation module is used to generate a valve opening control signal based on the rate of change of the arc energy generated when the protection switch executes the power-off command over time, thereby controlling the fire-fighting equipment to release the extinguishing medium.

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