A low-temperature discharge control method and system based on a heated sandwich battery compartment

Abstract

The application relates to the technical field of battery control, in particular to a low-temperature discharge control method and system based on a heating interlayer battery compartment, which comprises the following steps: acquiring battery temperature data and preprocessing the battery temperature data to obtain a temperature abnormal area; calculating heat transfer direction and gradient change according to the temperature abnormal area, and determining gradient deviation information; comparing the gradient deviation information to obtain a deviation area position, activating an auxiliary heat source according to the deviation area position, and obtaining stable temperature data; adjusting battery discharge parameters to obtain battery discharge adjustment configuration; acquiring real-time environment temperature data and real-time battery temperature data, adjusting heating parameters, and obtaining dynamic heating parameters; predicting a temperature change trend based on the real-time battery temperature data to obtain a temperature prediction result; and iteratively optimizing the dynamic heating parameters based on the temperature prediction result to obtain final control parameters. The method realizes stable discharge control of the battery at low temperature.

Description

Technical Field

[0001] This invention relates to the field of battery control technology, and in particular to a low-temperature discharge control method and system based on a heated interlayer battery compartment. Background Technology

[0002] In the field of electric vehicles, battery performance management in low-temperature environments is a crucial research direction. In cold regions or extreme climates, the discharge efficiency and stability of batteries have a decisive impact on the reliability of the entire vehicle system.

[0003] In one existing technology, battery temperature data is first collected using a temperature sensor inside the battery compartment to identify areas with abnormal temperatures. An auxiliary heat source is then activated to heat these areas. During heating, fluctuations in battery output are monitored, and heating parameters are adjusted based on the monitoring results to obtain a discharge control strategy for low temperatures. However, when faced with complex and variable external environments, existing technologies cannot comprehensively consider factors such as ambient temperature and battery output power that affect battery temperature. This can easily lead to uneven temperature distribution within the battery, directly causing some areas to be too cold, affecting discharge capacity, and even causing instability in the internal structure.

[0004] In summary, existing technologies lack effective analysis of ambient temperature and battery output power, making it difficult to adjust heating and discharging strategies in real time according to dynamic changes, thus failing to cope with complex and ever-changing low-temperature environments. Summary of the Invention

[0005] This invention provides a low-temperature discharge control method and system based on a heated interlayer battery compartment to achieve stable discharge control of the battery at low temperatures.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a low-temperature discharge control method based on a heated interlayer battery compartment, comprising:

[0007] Obtain battery temperature data, preprocess the battery temperature data, and obtain temperature anomaly areas;

[0008] Based on the temperature anomaly region, calculate the heat transfer direction and gradient change, and determine the gradient deviation information;

[0009] The gradient deviation information is compared based on a preset deviation threshold to obtain the location of the deviation region. An auxiliary heat source is activated based on the location of the deviation region to obtain stable temperature data.

[0010] Adjust the battery discharge parameters based on the stable temperature data to obtain the battery discharge adjustment configuration;

[0011] Real-time ambient temperature data and real-time battery temperature data are acquired, and the heating parameters are adjusted in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters;

[0012] Based on the real-time battery temperature data, the temperature change trend is predicted to obtain the temperature prediction result. Based on the temperature prediction result, the dynamic heating parameters are iteratively optimized to obtain the final control parameters.

[0013] In one optional implementation, the step of acquiring battery temperature data and preprocessing the battery temperature data to obtain temperature anomaly regions includes:

[0014] Obtain battery temperature data, clean the battery temperature data, remove data that exceeds a preset cleaning threshold, and obtain cleaning temperature data.

[0015] Spatial interpolation is performed on the cleaning temperature data to obtain temperature distribution data;

[0016] If the temperature distribution data is lower than a preset temperature threshold, it is marked as abnormal, and an abnormal temperature region is obtained.

[0017] In one optional implementation, the step of calculating the heat transfer direction and gradient change based on the temperature anomaly region to determine gradient deviation information includes:

[0018] The direction and gradient of heat transfer are calculated based on the temperature anomaly region to obtain temperature gradient data.

[0019] If the temperature gradient data exceeds a preset gradient threshold, it is marked as a gradient deviation, and gradient deviation information is obtained.

[0020] In one optional implementation, the step of comparing the gradient deviation information based on a preset deviation threshold to obtain the location of the deviation region, and activating an auxiliary heat source according to the location of the deviation region to obtain stable temperature data includes:

[0021] If the temperature deviation information exceeds the preset deviation threshold, the area with the temperature deviation is located to obtain the location of the deviation area;

[0022] Based on the location of the deviation area, the parameters of the auxiliary heat source are configured to obtain a heat replenishment scheme;

[0023] Based on the heat replenishment scheme, heat is injected into the deviation area, and the temperature of the deviation area is monitored in real time to obtain the temperature adjustment data.

[0024] The regulated temperature data is compared with the preset adjustment threshold. If the regulated temperature data does not meet the preset adjustment threshold, the parameters of the auxiliary heat source are adjusted to obtain stable temperature data.

[0025] In one optional implementation, adjusting the battery discharge parameters based on the stable temperature data to obtain the battery discharge adjustment configuration includes:

[0026] The stable temperature data is processed into layers to obtain temperature layering features;

[0027] If the temperature stratification characteristics deviate from the preset stratification standard, the battery discharge parameters are adjusted to obtain the adjusted discharge parameters;

[0028] Obtain battery output data, and optimize the adjusted discharge parameters based on the battery output data to obtain the battery discharge adjustment configuration.

[0029] In one optional implementation, the step of acquiring real-time ambient temperature data and real-time battery temperature data, and adjusting the heating parameters in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters, includes:

[0030] Acquire real-time ambient temperature data and real-time battery temperature data, calculate the difference between the real-time battery temperature data and the real-time ambient temperature data, and obtain real-time temperature difference data;

[0031] If the real-time temperature difference data exceeds the preset temperature difference threshold, the heating parameters are corrected to obtain temperature difference correction parameters;

[0032] The battery discharge state under the battery discharge adjustment configuration is monitored to obtain the voltage fluctuation state. If the voltage fluctuation state exceeds the preset voltage threshold, the temperature difference correction parameter is adjusted to obtain the dynamic heating parameter.

[0033] In one optional implementation, the step of predicting the temperature change trend based on the real-time battery temperature data to obtain a temperature prediction result, and then iteratively optimizing the dynamic heating parameters based on the temperature prediction result to obtain the final control parameters, includes:

[0034] Based on the real-time battery temperature data, the future temperature change trend is predicted, and the temperature prediction result is obtained.

[0035] The temperature prediction results are compared with the preset prediction threshold. If the temperature prediction results exceed the preset prediction threshold, the dynamic heating parameters are adjusted until the temperature prediction results meet the preset prediction threshold, and the final control parameters are obtained.

[0036] Secondly, the present invention provides a low-temperature discharge control system based on a heated interlayer battery compartment, comprising:

[0037] The data preprocessing module is used to acquire battery temperature data, preprocess the battery temperature data, and obtain temperature anomaly areas.

[0038] The gradient calculation module calculates the direction of heat transfer and gradient change based on the temperature anomaly region, and determines the gradient deviation information.

[0039] The heat source activation module is used to compare the gradient deviation information based on a preset deviation threshold to obtain the location of the deviation region, and activate the auxiliary heat source according to the location of the deviation region to obtain stable temperature data.

[0040] The discharge adjustment module is used to adjust the battery discharge parameters according to the stable temperature data to obtain the battery discharge adjustment configuration.

[0041] The dynamic heating module is used to acquire real-time ambient temperature data and real-time battery temperature data, and adjust the heating parameters in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters.

[0042] The iterative optimization module is used to predict the temperature change trend based on the real-time battery temperature data, obtain the temperature prediction result, and iteratively optimize the dynamic heating parameters based on the temperature prediction result to obtain the final control parameters.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] According to the patent document, the following are four beneficial effects of a low-temperature discharge control method based on a heated interlayer battery compartment:

[0045] (1) This invention achieves accurate reconstruction of the internal temperature field of the battery by cleaning, spatial interpolating and identifying abnormal areas of the multi-point temperature data of the battery. Compared with the traditional method that relies on single-point temperature measurement, this method can accurately identify the real temperature abnormal area and avoid inaccurate judgment due to sensor error or data fluctuation, thereby improving the accuracy and reliability of battery thermal condition analysis under low temperature conditions.

[0046] (2) This invention calculates the heat flow path and performs gradient analysis on the temperature anomaly area to generate accurate temperature deviation information, and activates the local auxiliary heat source of the heating interlayer based on the deviation threshold to achieve point-to-point heating of the colder area. This method avoids the problems of high energy consumption, low efficiency and uneven temperature caused by traditional overall heating methods, and achieves a more efficient and safer low-temperature heating process.

[0047] (3) This invention dynamically adjusts the battery discharge parameters based on the stable temperature data after heating, and performs secondary calibration of the heating strategy by combining the real-time ambient temperature, real-time battery temperature and voltage fluctuation, thereby generating dynamic heating parameters. By incorporating the internal state of the battery and external environmental factors into the decision-making process, this method overcomes the problem that existing technologies cannot adapt to rapid changes in external temperature, thus achieving adaptive optimization of the discharge strategy and improving the stability of low-temperature discharge.

[0048] (4) The present invention performs temperature change trend prediction based on dynamic heating parameters and iteratively optimizes the prediction results to form the final control strategy. By introducing the feedforward mechanism of "future temperature trend prediction" in the control process, the present invention can identify potential secondary cooling or abnormal fluctuation risks in advance, avoid battery performance degradation or safety hazards at low temperatures, and improve the safety and reliable operation of the battery system under low temperature conditions. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a low-temperature discharge control method based on a heated interlayer battery compartment provided in an embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of a low-temperature discharge control system based on a heated interlayer battery compartment provided in an embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Reference Figure 1 This invention provides a low-temperature discharge control method based on a heated interlayer battery compartment, comprising:

[0053] S11, acquire battery temperature data, preprocess the battery temperature data to obtain temperature abnormality areas;

[0054] S12, based on the temperature anomaly region, calculate the heat transfer direction and gradient change, and determine the gradient deviation information;

[0055] S13, compare the temperature deviation information based on the preset deviation threshold to obtain the location of the deviation area, and activate the auxiliary heat source according to the location of the deviation area to obtain stable temperature data;

[0056] S14, adjust the battery discharge parameters according to the stable temperature data to obtain the battery discharge adjustment configuration;

[0057] S15, acquire real-time ambient temperature data and real-time battery temperature data, and adjust the heating parameters in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters;

[0058] S16, based on the real-time battery temperature data, predict the temperature change trend to obtain the temperature prediction result, and iteratively optimize the dynamic heating parameters based on the temperature prediction result to obtain the final control parameters.

[0059] In step S11, battery temperature data is acquired, and the battery temperature data is preprocessed to obtain temperature anomaly regions, including:

[0060] Obtain battery temperature data, clean the battery temperature data, remove data that exceeds a preset cleaning threshold, and obtain cleaning temperature data.

[0061] Spatial interpolation is performed on the cleaning temperature data to obtain temperature distribution data;

[0062] If the temperature distribution data is lower than a preset temperature threshold, it is marked as abnormal, and an abnormal temperature region is obtained.

[0063] It should be noted that, firstly, battery temperature data is acquired through temperature sensors deployed inside the battery compartment, which collect temperature values ​​for their respective areas every 30 seconds. These temperature sensors are distributed throughout the battery compartment, particularly monitoring the positive and negative electrode areas of individual cells, the outer edge of the battery pack, and the coolant channel areas to ensure the battery operates within a safe temperature range. The temperature sensors are deployed according to pre-defined three-dimensional coordinates within the battery. During battery temperature data acquisition, the temperature value collected by the sensor, its coordinates, and the acquisition time are all bound together as a single data point. Furthermore, the battery temperature data is cleaned. This cleaning process includes removing abnormal data points whose temperature values ​​exceed a preset cleaning threshold and replacing the abnormal data points with the temperature value collected by the same sensor at the previous time point.

[0064] Subsequently, temperature data points within the same time period are integrated to obtain battery temperature data. The time integration adopts a fixed time window method, with half a minute as the baseline, and every 30-second interval as a processing window, such as 9:15:01-9:15:30, 9:15:31-9:16:00, etc., and the start time of the time period is used for marking.

[0065] For example, in a single battery temperature data acquisition, taking the right-angled vertex near the coolant channel area at the bottom of the battery compartment as the origin and using 1 cm as the unit length, sensor A with three-dimensional coordinates (14, 12, 2) acquired a temperature of 25.2℃ at 8:45:33, obtaining the data point [(14, 12, 2), 8:45:33, 25.2℃]; sensor B with three-dimensional coordinates (23, 2, 6) acquired a temperature of 25.7℃ at 8:45:44, obtaining the data point [(23, 2, 6)]. The sensor C with three-dimensional coordinates (2, 8, 10) collected a temperature of 25.4℃ at 8:45:52, resulting in the data point [(2, 8, 10), 8:45:52, 25.4℃]. These three data points were collected within the same time window, so the three data points were integrated to obtain the battery temperature data for the period from 8:45:31 to 8:46:00, and their timestamps were uniformly changed to the start time of this period, i.e., 8:45:31.

[0066] The preset cleaning threshold is determined by the battery's chemical characteristics. Generally, the acceptable operating temperature of a battery is -10°C to 40°C. When the temperature exceeds 60°C, the battery has exceeded its critical temperature, and harmful chemical reactions may occur internally, leading to battery damage or spontaneous combustion. Below -20°C, the battery's operating state is extremely unstable; operating it under these conditions may cause internal short circuits due to lithium plating, potentially triggering thermal runaway. Therefore, the upper limit of the preset temperature threshold is set to 60°C, and the lower limit is set to -20°C.

[0067] Further, spatial interpolation is performed on the cleaning temperature data to obtain temperature distribution data. This spatial interpolation includes calculating the Euclidean distance between the coordinates of each temperature sensor and the coordinates of all other temperature sensors, and then integrating this data to obtain Euclidean distance data. The distance can be calculated using the Euclidean distance formula:

[0068]

[0069] Where (x1, y1, z1) and (x2, y2, z2) are the three-dimensional coordinates of the two temperature sensors. d represents their Euclidean distance. Subsequently, for each temperature sensor's coordinates, the coordinate point of the temperature sensor with the smallest Euclidean distance is selected, and the midpoint coordinates of the shortest line segment containing them are calculated, using the following formula:

[0070]

[0071] Where, x mind y mind and z mindThe midpoint is defined by its three-dimensional coordinates. If the coordinates of the two temperature sensors are the points with the smallest Euclidean distance between them, then the midpoint coordinates only need to be calculated once. The arithmetic mean of the temperature values ​​collected by the two temperature sensors and the time interval in which the data was collected are interpolated to the calculated midpoint coordinates to form a new battery temperature data point.

[0072] For example, in a single spatial interpolation, sensor A (coordinates (2, 12, 2), with a collected temperature of 24.7℃ and a collection time period of 10:10:31-10:11:00) and sensor B (coordinates (4, 8, 2), with a collected temperature of 24.3℃ and a collection time period of 10:10:31-10:11:00) are the sensor coordinates with the smallest Euclidean distance to each other. First, their midpoint coordinates x are calculated. mind =(2+4) / 2=3, y mind =(12+8) / 2=10, z mind =(2+2) / 2=2, so the midpoint coordinates are (3, 10, 2). The arithmetic mean of the temperatures collected by the two temperature sensors is (24.7℃+24.3℃) / 2=24.5℃. From this, we can obtain the new battery temperature data point [(3, 10, 2), 10:10:31, 24.5℃].

[0073] Further, a threshold comparison is performed on the temperature distribution data. If the temperature distribution data is lower than a preset temperature threshold, it is marked as abnormal, thus obtaining a temperature abnormality region. The preset temperature threshold is determined by the arithmetic mean of the battery temperatures in the current temperature distribution data. Generally, the battery pack should control the internal temperature difference within ±5℃. First, the arithmetic mean of the battery temperatures in the current temperature distribution data is calculated. Temperatures 5℃ below the arithmetic mean are set as the preset temperature threshold. Temperature data below the preset temperature threshold are marked as temperature abnormalities, thus obtaining a temperature abnormality region. For example, in a set of temperature distribution data, there are 6 data points with temperature values ​​of 24.1℃, 25.2℃, 19.3℃, 26.7℃, 26.2℃, and 25.8℃. Their arithmetic mean is 24.55℃, so the preset temperature threshold is set to 19.55℃. 19.3℃ is lower than the preset temperature threshold and is marked as a temperature abnormality. All data points marked as abnormal are then integrated to obtain the temperature abnormality region.

[0074] In step S12, calculating the heat transfer direction and gradient change based on the temperature anomaly region and determining the gradient deviation information includes:

[0075] The direction and gradient of heat transfer are calculated based on the temperature anomaly region to obtain temperature gradient data.

[0076] If the temperature gradient data exceeds a preset gradient threshold, it is marked as a gradient deviation, and gradient deviation information is obtained.

[0077] First, read the temperature anomaly area. For the data points marked as having temperature anomalies, calculate the Euclidean distance Δd between the coordinates of this data point and the coordinates of all other data points. The Euclidean distance calculation formula is the same as the Euclidean distance calculation formula in step S11, but the distance unit is uniformly set to meters (m). The coordinate origin and unit length are consistent with step S11 (the right-angle vertex of the bottom of the battery compartment near the coolant channel area is the coordinate origin, and the unit length is 1 centimeter), but when calculating the Euclidean distance, the coordinate values ​​need to be converted to meters (m) to ensure that the unit of Δd is meters.

[0078] Subsequently, data points whose Euclidean distance is less than or equal to a preset adjacent threshold are selected and marked as adjacent data points. The preset adjacent threshold is determined based on the physical structural characteristics of the battery compartment and the effective monitoring range of the temperature sensor. In electric vehicle battery packs, the size of a single cell is typically 10-15 cm. Setting the preset adjacent threshold to 20 cm ensures that each abnormal temperature data point can capture temperature information from at least 1-2 adjacent cell locations. In practical applications, this threshold can be adjusted according to the specific layout of the battery pack. For example, when the average cell spacing is less than 10 cm, the threshold can be appropriately reduced to 15 cm; when the spacing is greater than 15 cm, the threshold can be increased to 25 cm.

[0079] Based on each outlier data point and its neighboring data points, the temperature gradient between them is calculated. The formula for calculating the temperature gradient is as follows:

[0080]

[0081] Among them, T neighbor T represents the temperature values ​​of adjacent data points. anomaly Here, represents the temperature value of the outlier data point, and Δd is the Euclidean distance between the two points. Temperature gradient. It shows the relationship between the temperature difference and spatial distance between abnormal data points and adjacent data points, and can reflect the rate of change of heat transfer.

[0082] when When the value is positive, it indicates that the temperature at the coordinates of the outlier data point is lower than the temperature at the coordinates of the adjacent data point, and heat is being transferred to the coordinates of the outlier data point. When the value is negative, it indicates that the temperature at the coordinates of the abnormal data point is higher than the temperature at the coordinates of the adjacent data point, and heat is being transferred to the coordinates of the adjacent data point.

[0083] Furthermore, a threshold comparison is performed on the temperature gradient data. The preset gradient threshold is set based on the battery thermal conduction theory, with a reasonable range of 0.1–1°C / m. To ensure control sensitivity, the preset gradient threshold is set to 1°C / m. If the temperature gradient data exceeds the preset gradient threshold, it is marked as a gradient deviation. The heat transfer direction vector, the coordinates of abnormal data points, and the temperature gradient are integrated to obtain the temperature gradient data.

[0084] For example, in a temperature gradient calculation, there are four data points, with coordinates uniformly expressed in cm. Data point A has coordinates (14, 12, 2) and a temperature of 16.3°C; data point B has coordinates (16, 11, 5) and a temperature of 20.2°C; data point C has coordinates (1, 1, 2) and a temperature of 24.5°C; and data point D has coordinates (40, 25, 12) and a temperature of 25.2°C. Data point A is marked as a temperature anomaly. First, the coordinates are converted to meters (m): A (0.14, 0.12, 0.02) m, B (0.16, 0.11, 0.05) m, C (0.01, 0.01, 0.02) m, and D (0.40, 0.25, 0.12) m.

[0085] Calculate the Euclidean distance between data points A and B:

[0086]

[0087] Euclidean distance between data points A and C:

[0088]

[0089] The Euclidean distance between data points A and D exceeds the preset adjacent threshold of 0.2m; therefore, only the gradients between A and B, and between A and C, are calculated. The temperature gradient is calculated as follows:

[0090] The gradients of A and B are The heat transfer direction vector is (0.16-0.14, 0.11-0.12, 0.05-0.02) = (0.02, -0.01, 0.03).

[0091] The gradients of A and C are The heat transfer direction vector is (0.01-0.14, 0.01-0.12, 0.02-0.02) = (-0.13, -0.11, 0).

[0092] because and All exceeded the preset gradient threshold of 1°C / m, and were therefore marked as gradient deviations. The integrated temperature gradient data included vector (0.02, -0.01, 0.03), coordinates (0.14, 0.12, 0.02)m, and gradient 104.28°C / m, and vector (-0.13, -0.11, 0), coordinates (0.14, 0.12, 0.02)m, and gradient 48.15°C / m.

[0093] In step S13, the gradient deviation information is compared based on a preset deviation threshold to obtain the location of the deviation region. An auxiliary heat source is then activated based on the location of the deviation region to obtain stable temperature data, including:

[0094] If the temperature deviation information exceeds the preset deviation threshold, the area with the temperature deviation is located to obtain the location of the deviation area;

[0095] Based on the location of the deviation area, the parameters of the auxiliary heat source are configured to obtain a heat replenishment scheme;

[0096] Based on the heat replenishment scheme, heat is injected into the deviation area, and the temperature of the deviation area is monitored in real time to obtain the temperature adjustment data.

[0097] The regulated temperature data is compared with the preset adjustment threshold. If the regulated temperature data does not meet the preset adjustment threshold, the parameters of the auxiliary heat source are adjusted to obtain stable temperature data.

[0098] It should be noted that the gradient deviation information is calculated based on abnormal temperature data points. When multiple deviation gradients exist for an abnormal temperature data point, it indicates an abnormal temperature change at that location, potentially posing a risk of excessively rapid cooling. In this case, the system should compare the number of deviation gradients against a preset deviation threshold and activate an auxiliary heat source to prevent the battery temperature from dropping too quickly. The preset deviation threshold is typically set to three. If an abnormal temperature data point has three or more deviation gradients, the coordinates of that abnormal temperature data point are marked as a temperature deviation. After marking, all temperature deviation data points are integrated to obtain the location of the deviation region.

[0099] Furthermore, based on the location of the deviation area, the auxiliary heat source is parameter-configured to obtain a heat replenishment scheme. First, based on the three-dimensional coordinates of the temperature deviation data points, the nearest auxiliary heat source unit is located in the heating interlayer. The Euclidean distance between the coordinates of the temperature deviation data points and the coordinates of each auxiliary heat source is calculated using the Euclidean formula. If the number of deviation gradients is 3, the heat source unit with the smallest Euclidean distance is selected as the heating source; if the number of deviation gradients is 4 or 5, the two heat source units with the smallest Euclidean distance are selected as the heating sources; if the number of deviation gradients is greater than or equal to 6, the three heat source units with the smallest Euclidean distance are selected as the heating sources. The heating power is configured according to the following formula:

[0100]

[0101] Where P0 is the default heating power, which is typically set to 50W, and T... target It is the target heating temperature value, T target Set to the arithmetic mean of temperature values ​​in the battery temperature data. T current This is the temperature value of the current abnormal temperature data point. P is the initial heating power, and the calculated result is rounded to the nearest integer. k is the temperature difference correction coefficient. Since the normal operating temperature difference of the battery is generally within 5℃, when the temperature difference ΔT = 10℃, it is desirable to reach the upper limit of 80W. Calculated using the formula P = 50 × (1 + k × 10) ≈ 80, we get k ≈ 0.6 / 10 = 0.06℃. -1 Considering the nonlinear characteristics of the system response, k is actually taken as 0.1℃. -1 This is a more reasonable approach, as it better balances heating speed and stability, avoiding overshoot or oscillation.

[0102] Furthermore, safety limits are set for the heat sources, including a maximum power of no more than 80W for a single heat source; a temperature rise rate of no more than 0.05℃ / second in any area; and a safety upper limit for temperature of the current temperature + 10℃, after which the power P is immediately reduced to half of its original value. For example, in a single heating parameter configuration, for a data point with four deviation gradient temperature deviations, heat source unit A and heat source unit B with the smallest Euclidean distance are selected. The current temperature is 18.8℃, and the target temperature is 25.2℃. The heating power of the two heat source units is 50×[1+0.1×(25.2-18.8)]=82W, which exceeds the maximum power of a single heat source. Therefore, the heating power of both heat sources is set to 80W.

[0103] It is worth noting that, based on the heat replenishment scheme, heat is injected into the deviation area, and the temperature of the deviation area is monitored in real time to obtain regulated temperature data. First, according to the list of activated heat sources determined in the heat replenishment scheme, a start command is sent to the corresponding auxiliary heat source unit. The start command includes parameters such as heat source number, initial heating power, and safety constraints. After receiving the start command, the auxiliary heat source begins to inject targeted heat into the deviation area according to the configured power parameters.

[0104] During the heat injection process, the system initiates a real-time temperature monitoring mechanism. Specifically, firstly, the Euclidean distance between the coordinates of all temperature sensors and the coordinates of temperature deviation data points is calculated. Temperature sensors with an Euclidean distance less than 20 cm are selected, and the acquisition frequency of these sensors is increased from once every 30 seconds to once every 10 seconds to achieve high-frequency monitoring of the heating process. Each acquired temperature data point is bound to the spatial coordinates and acquisition timestamp of the temperature sensor, forming a real-time temperature data point. The continuously acquired real-time temperature data points are arranged according to a time series to obtain a temperature change curve. Further, based on the temperature change curve, the rate of temperature change between two adjacent acquisitions is calculated. The formula for calculating the rate of temperature change is:

[0105]

[0106] Among them, T t Let T be the temperature value at time t. t+Δt Let t be the temperature value at time t+Δt, where Δt is the data acquisition time interval (10 seconds). The collected real-time temperature data points and temperature change rate are integrated to form regulated temperature data. Further, the regulated temperature data is compared against a preset regulation threshold. If the regulated temperature data does not meet the preset regulation threshold, the parameters of the auxiliary heat source are adjusted to obtain stable temperature data. The preset regulation threshold includes two judgment criteria: a temperature threshold and a temperature change rate threshold. The temperature threshold is set within ±2℃ of the target temperature. If the current temperature at the deviation location falls within 2℃ of the target temperature, the temperature is considered to be within the acceptable range. Based on lithium battery thermal safety standards and actual test data, the temperature change rate threshold is set to 0.01℃ / second to 0.05℃ / second. This range ensures no risk of thermal runaway during continuous heating; the upper limit of 0.05℃ / second is equivalent to 3℃ / minute, lower than the typical thermal runaway threshold of 5℃ / minute. Simultaneously, it avoids excessively conservative thresholds leading to low heating efficiency, while the lower limit of 0.01℃ / second ensures basic temperature control sensitivity. When the temperature change rate is less than 0.01℃ / second, it indicates that the temperature is stabilizing. When the temperature change rate is greater than 0.05℃ / second, it indicates that the heating rate is too fast and there is a safety risk.

[0107] It should be noted that the parameters in the adjusted temperature data are compared with the preset adjustment threshold. If the current temperature at the deviation area does not reach the temperature threshold range, it is determined that the temperature is not up to standard and heating needs to continue. If the temperature change rate exceeds the upper limit of the temperature change rate threshold, it is determined that the heating speed is too fast. If the temperature change rate is lower than the lower limit of the temperature change rate threshold and the temperature is not up to standard, it is determined that the heating effect is insufficient.

[0108] When the regulated temperature data is determined to be inconsistent with the preset regulation threshold, a parameter adjustment mechanism is automatically triggered. Specifically, if the heating effect is insufficient, the current heating power is reduced by 20%. If the temperature rise is too slow or stagnant, the current heating power is increased by 20%, but it must be ensured that it does not exceed the maximum power limit of 80W for a single heat source. After the parameter adjustment is completed, the heat injection and real-time monitoring process continues, the regulated temperature data is re-collected and threshold comparisons are performed, and this process is repeated until all indicators of the regulated temperature data meet the requirements of the preset regulation threshold. At this point, it can be considered a stable state. The battery temperature data collected in the current state are then integrated to obtain the stable temperature data.

[0109] In step S14, the battery discharge parameters are adjusted according to the stable temperature data to obtain the battery discharge adjustment configuration, including:

[0110] The stable temperature data is processed into layers to obtain temperature layering features;

[0111] If the temperature stratification characteristics deviate from the preset stratification standard, the battery discharge parameters are adjusted to obtain the adjusted discharge parameters;

[0112] Obtain battery output data, and optimize the adjusted discharge parameters based on the battery output data to obtain the battery discharge adjustment configuration.

[0113] The stratified processing refers to classifying and statistically analyzing the stable temperature data collected by all temperature sensors within the battery compartment according to their temperature ranges to comprehensively assess the overall temperature distribution inside the battery. Specifically, the temperature data is divided into five temperature levels: extremely low temperature (below 10°C), low temperature (between 10°C and 20°C), suitable temperature (between 20°C and 30°C), high temperature (between 30°C and 40°C), and extremely high temperature (above 40°C). The number of temperature data points in each temperature level is counted, and the percentage of data points in each level relative to the total number of data points is calculated. The percentage calculation formula is as follows:

[0114]

[0115] Among them, P i N represents the percentage of data points at the i-th temperature level. iN represents the number of data points contained in the i-th temperature level. total This represents the total number of temperature data points. By calculating the proportion of each temperature level, the overall temperature distribution inside the battery can be intuitively reflected. For example, in one temperature stratification statistical analysis, a total of 50 stable temperature data points were collected, including 0 data points in the extremely low temperature layer, 15 data points in the low temperature layer, 30 data points in the suitable temperature layer, 4 data points in the high temperature layer, and 1 data point in the extremely high temperature layer. The proportions of each level were calculated using the following formulas: Extremely low temperature layer P1 = 0 ÷ 50 = 0%, Low temperature layer P2 = 15 ÷ 50 = 30%, Suitable temperature layer P3 = 30 ÷ 50 = 60%, High temperature layer P4 = 4 ÷ 50 = 8%, Extremely high temperature layer P5 = 1 ÷ 50 = 2%. Integrating the proportions of each temperature level yields the temperature stratification characteristics.

[0116] Furthermore, the temperature stratification characteristics are compared based on a preset stratification standard. This preset stratification standard is a temperature distribution benchmark established according to the battery's optimal operating temperature range and safe operation requirements. Specifically, the preset stratification standard stipulates that the proportion of data points in the optimal temperature layer (20℃ to 30℃) should not be less than 60%. If the temperature stratification characteristics deviate from the preset stratification standard, the battery discharge parameters are adjusted. These discharge parameters include discharge current and discharge power. The specific adjustment strategy is as follows:

[0117] When the proportion of the suitable temperature layer is less than 60% and the sum of the proportions of the low temperature layer and the ultra-low temperature layer is greater than 20%, it indicates that the overall battery temperature is too low, the electrochemical reaction activity inside the battery is insufficient, and the ion transport rate is slow. If the original discharge parameters are maintained, it may lead to problems such as insufficient battery output power and excessively rapid drop in terminal voltage. In this case, the battery discharge parameters are adjusted to reduce the discharge current and discharge power by 20%. For example, if the original discharge current is set to 100A, the adjusted discharge current is 80A; if the original discharge power is set to 5kW, the adjusted discharge power is 4kW.

[0118] When the proportion of the suitable temperature layer is less than 60% and the sum of the proportions of the high temperature layer and the ultra-high temperature layer is greater than 20%, it indicates that the overall battery temperature is too high, and there is a risk of overheating inside the battery. Sustained high temperature conditions may cause irreversible damage such as electrolyte decomposition and separator shrinkage, and in severe cases, it may even lead to thermal runaway. In this case, adjust the battery discharge parameters, reducing the discharge current and discharge power by 25%. For example, if the original discharge current is set to 100A, the adjusted discharge current will be 75A; if the original discharge power is set to 5kW, the adjusted discharge power will be 3.75kW.

[0119] In the remaining cases, the proportion of the suitable temperature layer is less than 60%, and the sum of the proportions of the high temperature layer and the extremely high temperature layer, as well as the sum of the proportions of the low temperature layer and the extremely low temperature layer, both exceed 20%. This indicates that the internal temperature distribution of the battery is uneven, with some areas potentially over-discharged while other areas are underutilized. This imbalance accelerates battery aging. In this case, the battery discharge parameters are adjusted by reducing the discharge current and discharge power by 15%. For example, if the original discharge current is set to 100A, the adjusted discharge current is 85A; if the original discharge power is set to 5kW, the adjusted discharge power is 4.25kW. After completing the above adjustments, the adjusted discharge parameters are obtained.

[0120] Further, battery output data is acquired, including real-time output voltage, which is collected in real time by a voltage sensor deployed at the battery output terminal. Further, the discharge parameters are optimized based on the battery output data. First, battery output voltage data is continuously collected within a 20-second time window, at a frequency of 5 times per second. Then, the collected voltage data is arranged in time sequence to form a voltage dataset {V1, V2, V3, ..., V...}. n Next, calculate the maximum voltage V within this time window. max and minimum value V min The formula for calculating voltage fluctuation amplitude is as follows:

[0121]

[0122] Among them, V rated This refers to the rated voltage of the battery. For example, if the rated voltage of a battery is 400V, and the maximum voltage collected within a 20-second time window is 408V and the minimum voltage is 394V, then the voltage fluctuation range η = (408-394) / 400 × 100% = 3.5%.

[0123] If the voltage fluctuation exceeds 5% of the rated voltage, it indicates that the adjusted discharge parameters are still not suitable for the current battery state. The battery output current and output power are reduced by 2%, and the monitoring is performed again. If the voltage fluctuation still exceeds 5% of the rated voltage, the battery output current and output power are reduced by 2% again. Through the above multiple rounds of iterative optimization, the adjusted discharge parameters maximize the battery performance while ensuring the battery's safe operation, and finally the battery discharge adjustment configuration is obtained.

[0124] In step S15, real-time ambient temperature data and real-time battery temperature data are acquired, and the heating parameters are adjusted in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters, including:

[0125] Acquire real-time ambient temperature data and real-time battery temperature data, calculate the difference between the real-time battery temperature data and the real-time ambient temperature data, and obtain real-time temperature difference data;

[0126] If the real-time temperature difference data exceeds the preset temperature difference threshold, the heating parameters are corrected to obtain temperature difference correction parameters;

[0127] The battery discharge state under the battery discharge adjustment configuration is monitored to obtain the voltage fluctuation state. If the voltage fluctuation state exceeds the preset voltage threshold, the temperature difference correction parameter is adjusted to obtain the dynamic heating parameter.

[0128] First, real-time ambient temperature data and real-time battery temperature data are acquired. The real-time ambient temperature data is collected by ambient temperature sensors deployed under the vehicle chassis, on the surface of the battery compartment shell, and at the vehicle's air intakes, comprehensively reflecting the external thermal environment of the battery compartment. The ambient temperature sensors are set to collect data every 15 seconds to ensure timely detection of rapid changes in ambient temperature. For example, in cold winter regions, when a vehicle moves from a warm underground garage into a low-temperature outdoor environment, the ambient temperature may drop sharply from 5°C to -15°C in a short period; high-frequency data acquisition can quickly detect this change.

[0129] Simultaneously, real-time battery temperature data is acquired. This real-time battery temperature data is still collected via a temperature sensor array inside the battery compartment, with the acquisition frequency synchronized with the ambient temperature sensor, also every 15 seconds. To ensure data consistency, the system employs a unified timestamp management mechanism, pairing and binding ambient temperature data and battery temperature data collected within the same time window. Specifically, with a 15-second acquisition cycle, such as 10:30:01-10:30:15, 10:30:16-10:30:30, etc., the arithmetic mean of all ambient temperature data collected within that time window is taken as the real-time ambient temperature T for that cycle. env The arithmetic mean of all battery temperature data is taken as the real-time battery temperature T for that cycle. bat Calculate the real-time battery temperature T env With the real-time ambient temperature T bat The difference is used to obtain real-time temperature difference data.

[0130]

[0131] Where, ΔT real For real-time temperature difference data, T bat For real-time battery temperature, T envThe real-time ambient temperature is used. Further, based on the calculated real-time temperature difference data, a preset temperature difference threshold is compared. This preset temperature difference threshold is determined according to the design requirements of the battery thermal management system and the battery's thermal balance characteristics under different operating conditions. Generally, when the battery is in normal operating condition, a relatively stable temperature difference range should be maintained between the battery's internal temperature and the ambient temperature. Considering the battery's operating temperature and ambient temperature in winter, the lower limit of the preset temperature difference threshold is generally set to 10℃, and the upper limit is set to 35℃. If the real-time temperature difference data exceeds the preset temperature difference threshold, the heating parameters are corrected. The heating parameter correction includes dynamically adjusting the heating power and heating duration of the auxiliary heat source. The specific correction strategy is as follows:

[0132] When ΔT real When the temperature is below 10℃, it indicates that the temperature difference between the battery and the environment is too small, and the system adopts a power reduction strategy. The heating power of all currently activated auxiliary heat sources is uniformly reduced by 15%. Simultaneously, the heating duration monitoring cycle is extended from evaluating the heating effect every 20 seconds to evaluating it every 30 seconds, in order to slow down the heating rate and prevent the battery temperature from rising too quickly. For example, if the current heating power of an auxiliary heat source is 60W, the corrected heating power is adjusted to 60 × (1 - 15%) = 51W.

[0133] When ΔT real When the temperature exceeds 35℃, it indicates that the temperature difference between the battery and the environment is too large, and the battery is facing the risk of rapid heat dissipation. The system then adopts a power-increasing strategy. First, the temperature difference deviation is calculated using the following formula:

[0134]

[0135] Here, ξ represents the temperature difference deviation, used to quantify the severity of the real-time temperature difference exceeding the upper limit threshold. The formula for calculating the temperature difference deviation is derived based on the actual operating characteristics of the battery thermal management system and the graded control strategy. The specific derivation process is as follows: First, the upper limit of the preset temperature difference threshold is set to 35℃, which represents the safe boundary of the temperature difference between the battery and the environment. When the real-time temperature difference exceeds 35℃, a graded response is required based on the degree of exceedance. Subtracting 35℃ is to calculate the absolute value of the temperature difference exceeding the safe upper limit. Second, dividing by 10℃ establishes the graded standard for power adjustment. Based on battery thermal management experience, for every 10℃ temperature difference exceeding the limit, the system needs to increase the power level by one level.

[0136] Specifically, the temperature difference exceeding the standard is divided into three levels: 0-10℃ is a slight exceedance (ξ≤1); 10-20℃ is a moderate exceedance (1<ξ≤2); and above 20℃ is a severe exceedance (ξ>2). When ξ≤1, the heating power of all currently activated auxiliary heat sources will be uniformly increased by 20%; when 1<ξ≤2, the heating power will be increased by 30%; and when ξ>2, the heating power will be increased by 40%, but it must be ensured that the power of a single heat source does not exceed the maximum limit of 80W.

[0137] For example, if the real-time temperature difference ΔT real If the temperature is 42℃, then the temperature difference deviation ξ = (42-35) / 10 = 0.7, which is less than 1. Therefore, the heating power should be increased by 20%. If the current heating power of an auxiliary heat source is 55W, the adjusted heating power should be 55 × (1 + 20%) = 66W. After completing the above correction, the adjusted heating power and the heating time monitoring cycle should be integrated to obtain the temperature difference correction parameters.

[0138] It should be noted that the discharge state of the battery under the aforementioned battery discharge adjustment configuration is monitored by a voltage sensor deployed at the battery output terminal, continuously monitoring the battery's output voltage at a sampling frequency of 10 times per second for a duration of 30 seconds. The collected voltage data is arranged in a time series to form a voltage dataset {V}. 1， V 2， V 3， …, V n}, where n is the number of data collections within the time window, and n=300 within a 30-second monitoring period.

[0139] Furthermore, statistical analysis was performed on the voltage dataset to calculate the standard deviation σ of the voltage. V The formula is as follows:

[0140]

[0141] Among them, V i Let V be the voltage value at the i-th voltage data point. average This represents the arithmetic mean of the voltage dataset. The standard deviation σ of the voltage is... V Voltage fluctuation is a key indicator for evaluating battery discharge stability. To ensure a smooth and stable battery output voltage, a preset voltage threshold is typically set at 2% of the rated voltage. For example, if the battery's rated voltage is 400V, then the preset voltage threshold is 400 × 2% = 8V. If the voltage fluctuation (i.e., voltage standard deviation σ) is... V If the voltage exceeds the preset threshold, it indicates that the current temperature difference correction parameters have not yet reached the optimal state, the battery output performance is still unstable, and further adjustments to the temperature difference correction parameters are needed.

[0142] The specific adjustment strategy is as follows: when σV When the voltage is >8V, determine the main cause of the voltage fluctuation. If the real-time battery temperature T... bat If the temperature is below 20℃, it is considered insufficient heating, and the heating power of all heating units in the temperature difference correction parameters will be increased by 10%. If the real-time battery temperature T... bat If the temperature exceeds the upper limit of the target temperature range, it is considered overheating, and the heating power of all heating units in the temperature difference correction parameters is reduced by 10%. If the real-time battery temperature is within the target range but the voltage fluctuation is still large, it is considered uneven temperature distribution. In this case, it is necessary to return to step S11 to re-screen the abnormal temperature area of ​​the battery and activate the heat sources near the abnormal temperature area in the order of steps. After the above adjustments, voltage fluctuation monitoring continues for 30 seconds, the voltage standard deviation is recalculated and compared with the preset voltage threshold. Through multiple rounds of iterative optimization, until the voltage fluctuation state meets the preset voltage threshold requirement, the heating parameters are considered to have reached the optimal state. The system integrates the current heating power, the list of activated heat sources, and the heating duration monitoring cycle to obtain dynamic heating parameters.

[0143] In step S16, the temperature change trend is predicted based on the real-time battery temperature data to obtain a temperature prediction result. Based on the temperature prediction result, the dynamic heating parameters are iteratively optimized to obtain the final control parameters, including:

[0144] Based on the real-time battery temperature data, the future temperature change trend is predicted, and the temperature prediction result is obtained.

[0145] The temperature prediction results are compared with the preset prediction threshold. If the temperature prediction results exceed the preset prediction threshold, the dynamic heating parameters are adjusted until the temperature prediction results meet the preset prediction threshold, and the final control parameters are obtained.

[0146] First, historical data is collected and trend analysis is performed based on the real-time battery temperature data to predict future temperature trends. The prediction method is based on time series analysis principles, statistically modeling the changes in battery temperature over past periods to infer future temperature trends. First, historical temperature data sequences are collected. The system reads battery temperature data from the past 10 minutes from the data storage module. This data is processed in 15-second intervals, containing a total of 40 historical temperature data points. For each temperature sensor location within the battery compartment, an independent time series T is established. i (t)={T i，1， T i，2， T i，3 ,…,T i，40}, where i represents the sensor number, the subscript number represents the location index in the time series, and T i，1 The earliest temperature data, Ti，40 This is the temperature data at the current moment.

[0147] Furthermore, the historical temperature data series undergoes data preprocessing. First, a sliding window smoothing method is used to eliminate random fluctuations and measurement noise in the temperature data. The sliding window length is set to 3 data points. For each data point T in the series... i，k (k=2 to 39), calculate the arithmetic mean of the data and its adjacent data points as the smoothed data, as shown in the following formula:

[0148]

[0149] For the data points T at both ends of the sequence i，1 and T i，40 Keep the original values ​​unchanged. After smoothing, the denoised temperature sequence T is obtained. i (t) smooth .

[0150] Furthermore, based on the smoothed historical temperature series, a linear regression method was used to fit the temperature change trend. Using time as the independent variable x and temperature as the dependent variable y, a linear fitting model was established:

[0151]

[0152] Where 'a' is the slope of temperature change, representing the rate of temperature change per unit time; and 'b' is the intercept term. The least squares method is used to calculate the fitting parameters 'a' and 'b', and the calculation formula is as follows:

[0153]

[0154]

[0155] Where n is the number of historical data points, here n=40; ​​x k y is the time index corresponding to the k-th data point, with a value of k; k The smoothed temperature value T for the k-th data point k，smooth Furthermore, based on the fitted linear model, the temperature for future time periods is predicted. The prediction time window is set to 5 minutes, corresponding to 20 future time steps. For each temperature sensor location i, the predicted temperature value for the m-th future time step (m=1 to 20) is calculated using the following formula:

[0156]

[0157] Among them, a i and b iLet be the fitting parameters for the position of sensor i, and 40+m represent the absolute time index calculated from the starting point of the historical sequence. For example, a linear fit is performed on the historical temperature sequence of sensor A, yielding a slope a = -0.03℃ / time step and an intercept term b = 21.2℃. The predicted temperature at the 5th time step (i.e., 75 seconds later) is: T A，45，pred =(-0.03)×45+21.2=19.85℃; The predicted temperature at the 20th time step (i.e., 300 seconds later) is: T A，60，pred =(-0.03)×60+21.2=19.4℃. After the calculation is completed, at the end of the prediction time window (the 20th time step), the arithmetic mean T of the predicted values ​​of all temperature sensors at that moment is calculated. avg，pred And find the lowest temperature T among all the temperature sensor predictions at that moment. min，pred and the highest temperature T max，pred .

[0158] Based on the obtained temperature prediction results, a comparison is made with a preset prediction threshold to determine whether the current dynamic heating parameters can maintain the battery's thermal safety and performance stability in the future. The preset prediction threshold is a minimum temperature safety threshold T determined comprehensively based on the battery's safe operating temperature range and the control objectives of the thermal management system. min，safe Maximum temperature safety threshold T max，safe Minimum average temperature threshold T avg，min，safe and the highest average temperature threshold T avg，max，safe Generally, T min，safe Set to 15℃, and set T max，safe Set to 40℃, and set T avg，min，safe Set to 20℃, and set T avg，max，safe Set to 30℃

[0159] When T min，pred Less than T min，safe This indicates that the battery may experience localized overcooling in the future. Calculate T. min，pred The Euclidean distance between the corresponding temperature sensor and all heating units is used to select the heating unit with the smallest Euclidean distance and increase its heating power by 10%.

[0160] When T max，pred Greater than T max，safe This indicates that the battery may experience localized overheating in the future. Calculate T. max，pred The Euclidean distance between the corresponding temperature sensor and all heating units is used to select the heating unit with the smallest Euclidean distance and reduce its heating power by 10%.

[0161] When T avg，pred Less than T avg，min，safeThis indicates that the battery may experience overall overcooling in the future. The heating power of all heating units will be increased by 15%.

[0162] When T avg，pred Greater than T avg，max，safe This indicates that the battery may experience overall overheating in the future. Reduce the heating power of all heating units by 15%.

[0163] After completing the above adjustments, update the adjusted heating power and other parameters to the new dynamic heating parameters, and re-collect historical data and perform trend analysis until all characteristic parameters meet the threshold requirements. Then, determine the current dynamic heating parameters as the final control parameters.

[0164] In summary, this invention provides a low-temperature discharge control method based on a heated interlayer battery compartment, which can adjust the heating and discharge strategies in real time according to dynamic changes, thereby coping with complex and variable low-temperature environments and achieving stable discharge control of the battery at low temperatures.

[0165] Reference Figure 2 This invention provides a low-temperature discharge control system based on a heated interlayer battery compartment, comprising:

[0166] The data preprocessing module is used to acquire battery temperature data, preprocess the battery temperature data, and obtain temperature anomaly areas.

[0167] The gradient calculation module calculates the direction of heat transfer and gradient change based on the temperature anomaly region, and determines the gradient deviation information.

[0168] The heat source activation module is used to compare the gradient deviation information based on a preset deviation threshold to obtain the location of the deviation region, and activate the auxiliary heat source according to the location of the deviation region to obtain stable temperature data.

[0169] The discharge adjustment module is used to adjust the battery discharge parameters according to the stable temperature data to obtain the battery discharge adjustment configuration.

[0170] The dynamic heating module is used to acquire real-time ambient temperature data and real-time battery temperature data, and adjust the heating parameters in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters.

[0171] The iterative optimization module is used to predict the temperature change trend based on the real-time battery temperature data, obtain the temperature prediction result, and iteratively optimize the dynamic heating parameters based on the temperature prediction result to obtain the final control parameters.

[0172] It should be noted that the low-temperature discharge control system based on a heated sandwich battery compartment provided in this embodiment of the invention is used to execute all the process steps of the low-temperature discharge control method based on a heated sandwich battery compartment in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0173] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a low-temperature discharge control program based on a heated interlayer battery compartment. When the processor executes the computer program, it implements the steps in the various embodiments of the low-temperature discharge control method based on a heated interlayer battery compartment described above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the data preprocessing module.

[0174] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0175] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0176] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0177] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0178] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0179] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0180] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A low-temperature discharge control method based on a heated sandwich battery compartment, characterized in that, include: Obtain battery temperature data, preprocess the battery temperature data, and obtain temperature anomaly areas; Based on the temperature anomaly region, calculate the heat transfer direction and gradient change, and determine the gradient deviation information; The gradient deviation information is compared based on a preset deviation threshold to obtain the location of the deviation region. An auxiliary heat source is activated based on the location of the deviation region to obtain stable temperature data. Adjust the battery discharge parameters based on the stable temperature data to obtain the battery discharge adjustment configuration; Real-time ambient temperature data and real-time battery temperature data are acquired, and the heating parameters are adjusted in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters; Based on the real-time battery temperature data, the temperature change trend is predicted to obtain the temperature prediction result. Based on the temperature prediction result, the dynamic heating parameters are iteratively optimized to obtain the final control parameters. The step of comparing the gradient deviation information based on a preset deviation threshold to obtain the location of the deviation region, and activating the auxiliary heat source according to the location of the deviation region to obtain stable temperature data includes: if the gradient deviation information exceeds the preset deviation threshold, locating the region with gradient deviation to obtain the location of the deviation region; configuring the parameters of the auxiliary heat source according to the location of the deviation region to obtain a heat replenishment scheme; injecting heat into the location of the deviation region based on the heat replenishment scheme and monitoring the temperature of the location of the deviation region in real time to obtain regulated temperature data; comparing the regulated temperature data based on a preset regulation threshold, and if the regulated temperature data does not meet the preset regulation threshold, adjusting the parameters of the auxiliary heat source to obtain stable temperature data.

2. The low-temperature discharge control method based on a heated interlayer battery compartment according to claim 1, characterized in that, The process of acquiring battery temperature data and preprocessing the battery temperature data to obtain abnormal temperature regions includes: Obtain battery temperature data, clean the battery temperature data, remove data that exceeds a preset cleaning threshold, and obtain cleaning temperature data. Spatial interpolation is performed on the cleaning temperature data to obtain temperature distribution data; If the temperature distribution data is lower than a preset temperature threshold, it is marked as abnormal, and an abnormal temperature region is obtained.

3. The low-temperature discharge control method based on a heated interlayer battery compartment according to claim 1, characterized in that, The step of calculating the direction and gradient change of heat transfer based on the temperature anomaly region, and determining gradient deviation information, includes: The direction and gradient of heat transfer are calculated based on the temperature anomaly region to obtain temperature gradient data. If the temperature gradient data exceeds a preset gradient threshold, it is marked as a gradient deviation, and gradient deviation information is obtained.

4. The low-temperature discharge control method based on a heated interlayer battery compartment according to claim 1, characterized in that, The step of adjusting the battery discharge parameters based on the stable temperature data to obtain the battery discharge adjustment configuration includes: The stable temperature data is processed into layers to obtain temperature layering features; If the temperature stratification characteristics deviate from the preset stratification standard, the battery discharge parameters are adjusted to obtain the adjusted discharge parameters; Obtain battery output data, and optimize the adjusted discharge parameters based on the battery output data to obtain the battery discharge adjustment configuration.

5. The low-temperature discharge control method based on a heated interlayer battery compartment according to claim 1, characterized in that, The process of acquiring real-time ambient temperature data and real-time battery temperature data, and adjusting the heating parameters in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters, includes: Acquire real-time ambient temperature data and real-time battery temperature data, calculate the difference between the real-time battery temperature data and the real-time ambient temperature data, and obtain real-time temperature difference data; If the real-time temperature difference data exceeds the preset temperature difference threshold, the heating parameters are corrected to obtain temperature difference correction parameters; The battery discharge state under the battery discharge adjustment configuration is monitored to obtain the voltage fluctuation state. If the voltage fluctuation state exceeds the preset voltage threshold, the temperature difference correction parameter is adjusted to obtain the dynamic heating parameter.

6. The low-temperature discharge control method based on a heated interlayer battery compartment according to claim 5, characterized in that, The temperature change trend is predicted based on the real-time battery temperature data to obtain a temperature prediction result. The dynamic heating parameters are then iteratively optimized based on the temperature prediction result to obtain the final control parameters, including: Based on the real-time battery temperature data, the future temperature change trend is predicted, and the temperature prediction result is obtained. The temperature prediction results are compared with the preset prediction threshold. If the temperature prediction results exceed the preset prediction threshold, the dynamic heating parameters are adjusted until the temperature prediction results meet the preset prediction threshold, and the final control parameters are obtained.

7. A low-temperature discharge control system based on a heated sandwich battery compartment, characterized in that, include: The data preprocessing module is used to acquire battery temperature data, preprocess the battery temperature data, and obtain temperature anomaly areas. The gradient calculation module is used to calculate the direction of heat transfer and gradient change based on the temperature anomaly region, and to determine gradient deviation information. The heat source activation module is used to compare the gradient deviation information based on a preset deviation threshold to obtain the location of the deviation region, and activate the auxiliary heat source according to the location of the deviation region to obtain stable temperature data. The discharge adjustment module is used to adjust the battery discharge parameters according to the stable temperature data to obtain the battery discharge adjustment configuration. The dynamic heating module is used to acquire real-time ambient temperature data and real-time battery temperature data, and adjust the heating parameters in conjunction with the battery discharge adjustment configuration to obtain dynamic heating parameters. The iterative optimization module is used to predict the temperature change trend based on the real-time battery temperature data, obtain the temperature prediction result, and iteratively optimize the dynamic heating parameters based on the temperature prediction result to obtain the final control parameters. The step of comparing the gradient deviation information based on a preset deviation threshold to obtain the location of the deviation region, and activating the auxiliary heat source according to the location of the deviation region to obtain stable temperature data includes: if the gradient deviation information exceeds the preset deviation threshold, locating the region with gradient deviation to obtain the location of the deviation region; configuring the parameters of the auxiliary heat source according to the location of the deviation region to obtain a heat replenishment scheme; injecting heat into the location of the deviation region based on the heat replenishment scheme and monitoring the temperature of the location of the deviation region in real time to obtain regulated temperature data; comparing the regulated temperature data based on a preset regulation threshold, and if the regulated temperature data does not meet the preset regulation threshold, adjusting the parameters of the auxiliary heat source to obtain stable temperature data.

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