Intelligent temperature regulation and control system for new energy automobile battery

Abstract

The application relates to the field of new energy automobile battery temperature control technology and discloses a new energy automobile battery temperature intelligent regulation control system. A battery temperature data acquisition module of the system acquires real-time temperature data of a battery pack; a historical temperature section matching module filters a matching temperature section matched with the temperature intensity of the current battery working stage from historical temperature data; a temperature change key point analysis module analyzes the time sequence temperature change of the matching temperature section, determines a proportional regulation action point, and calculates the time delay amount from the starting point to the point; an adjustment matching degree analysis module compares the proportional regulation data sequence and the temperature data sequence change before and after the proportional regulation action point, and determines the adjustment matching degree; a weight calculation module determines the proportional regulation weight of the matching temperature section; and a temperature control module weights the proportional regulation coefficient of the matching temperature section according to the weight, obtains the proportional regulation coefficient of the current stage, inputs the real-time temperature data into a fuzzy controller, and controls the operation power of a battery cooling device.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology for new energy vehicle batteries, specifically to an intelligent temperature regulation and control system for new energy vehicle batteries. Background Technology

[0002] In the rapid development of the new energy vehicle industry, the battery, as the core power source, directly determines the vehicle's range, operational safety, and lifespan. Temperature is a key factor affecting battery performance. Batteries generate heat during charging and discharging. Excessive temperature can lead to thermal runaway, significantly reducing battery capacity and charging / discharging efficiency, and potentially causing fires and explosions. Conversely, excessively low temperatures significantly reduce battery activity, resulting in degraded charging / discharging performance and a drastically shortened driving range. Therefore, effectively regulating and controlling battery temperature has become a crucial issue in new energy vehicle technology research and development.

[0003] Most new energy vehicles on the market use simple threshold control for their battery temperature control systems. This involves pre-setting a maximum and minimum operating temperature threshold for the battery. When the battery temperature reaches the maximum threshold, the cooling system is activated to lower the temperature; when the temperature drops to the minimum threshold, the cooling system stops; and if the temperature falls below the minimum threshold, the heating system is activated. However, this control method has significant limitations. It relies solely on real-time temperature data for passive adjustment and fails to consider the differences in battery operating stages. Under different operating conditions, such as high-speed driving, low-speed congestion, fast charging, and slow charging, the rate of heat generation and the pattern of heat accumulation vary significantly. A single threshold control cannot adapt to diverse operating scenarios, often resulting in untimely adjustments. For example, during fast charging, the battery generates a large amount of heat in a short time. If cooling is activated according to the conventional threshold, the battery temperature may already be close to a dangerous range by the time the cooling system is activated, making it difficult to quickly control the temperature within a reasonable range. Conversely, during low-speed driving, the battery generates less heat, and if the cooling system operates at a fixed power after activation, it will result in energy waste.

[0004] Existing systems lack effective utilization of historical temperature data. Battery temperature changes exhibit certain regularities, and the temperature change trends at different operating stages often correlate with temperature changes under similar historical operating conditions. However, current control systems do not analyze and match historical temperature data, failing to extract effective adjustment experience from historical data, resulting in a lack of targeted adjustment strategies. Furthermore, existing adjustment systems often use fixed adjustment coefficients when determining the operating power of the cooling device, failing to optimize in conjunction with the dynamic process of temperature changes. For example, during periods of rapid battery temperature rise, fixed adjustment coefficients may not be sufficient to increase cooling intensity in a timely manner; conversely, when the rate of temperature rise slows down, over-cooling may occur. Some more complex temperature control systems, while attempting to introduce some adjustment parameters, fail to consider the matching degree between different historical temperature ranges and the current operating stage, nor do they conduct quantitative analysis of the adjustment effects. This leads to insufficient rationality and effectiveness of the adjustment strategies, making it difficult to achieve precise and efficient control of battery temperature, failing to fully utilize battery performance, and failing to meet users' high requirements for the range and safety of new energy vehicles. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent temperature regulation and control system for new energy vehicle batteries to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an intelligent temperature regulation and control system for new energy vehicle batteries, the system comprising:

[0007] The battery temperature data acquisition module is used to acquire real-time temperature data of the battery pack of new energy vehicles.

[0008] The historical temperature range matching module is used to filter out matching temperature ranges from historical temperature data that match the temperature intensity of the current battery operating stage.

[0009] The temperature change key point analysis module is used to analyze the temperature data changes of the matched temperature segment over time, determine the proportional adjustment action point of the matched temperature segment, and use the time difference between the starting point of each matched temperature segment and the proportional adjustment action point as the time delay of each matched temperature segment.

[0010] The adjustment matching degree analysis module is used to compare the changes in the proportional adjustment data sequence before the proportional adjustment action point with the temperature data sequence after the proportional adjustment action point to determine the adjustment matching degree of each matching temperature segment.

[0011] The weight calculation module is used to determine the proportional adjustment weight of the matching temperature range by combining the time delay, the degree of adjustment, and the temperature intensity difference between the matching temperature range and the current battery operating stage.

[0012] The temperature control module is used to weight the proportional adjustment coefficients of all matching temperature ranges corresponding to the current battery operating stage by proportional adjustment weights, and determine the proportional adjustment coefficient of the current battery operating stage; and input the proportional adjustment coefficient and real-time temperature data into the fuzzy controller, which controls the operating power of the battery cooling device.

[0013] Preferably, the historical temperature range matching module includes:

[0014] The temperature fluctuation analysis submodule is used to identify the affected parts of the temperature from historical temperature data sequences based on battery temperature fluctuations.

[0015] The temperature intensity calculation submodule is used to divide the historical temperature data sequence into multiple temperature data segments and determine the temperature intensity of each temperature data segment.

[0016] The matching and filtering submodule is used to filter out matching temperature ranges from the temperature-affected portions of historical temperature data that match the temperature intensity of the current battery operating phase.

[0017] Preferably, the temperature fluctuation analysis submodule includes:

[0018] The slope calculation unit is used to obtain the slope value corresponding to each temperature data in the historical temperature data sequence.

[0019] The division unit is used to divide the data based on the temperature data where the maximum slope value first appears.

[0020] The mean calculation unit is used to divide the temperature data sequence into a first part and a second part by dividing the data into two parts, and to calculate the mean slope of the first part and the second part of the temperature data sequence.

[0021] The affected part determination unit is used to identify the part with the largest average slope between the first and second parts as the temperature-affected part.

[0022] Preferably, the matching and filtering submodule includes:

[0023] The similarity mapping unit is used to negatively correlate each temperature data segment of the temperature-affected part of the historical temperature data with the temperature intensity difference of the current battery operating stage to obtain the temperature intensity similarity.

[0024] The matching segment determination unit is used to determine, based on the temperature intensity similarity, a temperature data segment of the affected part of the historical temperature data that matches the current battery operating stage, as the matching temperature segment.

[0025] Preferably, the temperature change key point analysis module includes:

[0026] The mutation point identification unit is used to identify the point where the temperature data of the matching temperature range changes abruptly for any matching temperature range corresponding to the current battery operating stage, and to use the point where the proportional adjustment of the matching temperature range occurs.

[0027] Preferably, the adjustment matching degree analysis module includes:

[0028] The proportional adjustment difference calculation unit is used to average the difference between the slope values ​​of two adjacent data in the proportional adjustment data sequence before the proportional adjustment action point for each matching temperature segment, and use the result value as the proportional adjustment difference.

[0029] The temperature difference calculation unit is used to average the difference between the slope values ​​of two adjacent data points in the temperature data sequence after the proportional adjustment point, and use the result value as the temperature difference.

[0030] The matching degree mapping unit is used to perform a negative correlation mapping on the difference between the proportional adjustment difference and the temperature difference, and use the result value as the adjustment matching degree of the matching temperature range.

[0031] Preferably, the weight calculation module includes:

[0032] The coordinate point determination unit is used to determine the coordinate point corresponding to each matching temperature segment by using the proportional adjustment coefficient of the proportional adjustment action point of each matching temperature segment as the abscissa and the time delay of each matching temperature segment as the ordinate.

[0033] The curve fitting unit is used to perform curve fitting on the coordinate points of all matching temperature ranges corresponding to the current battery operating stage, and obtain the fitted curve segment.

[0034] The residual acquisition unit is used to obtain the residual between the coordinate points of each matched temperature segment and the fitted curve segment;

[0035] An effectiveness determination unit is used to determine the effectiveness of each matching temperature range by combining the time delay, the adjustment matching degree, and the residual size.

[0036] The weighting determination unit is used to determine the proportional adjustment weight of the matching temperature range based on the effectiveness level and the temperature intensity difference between the matching temperature range and the current battery operating stage; wherein, the effectiveness level and the proportional adjustment weight are positively correlated; and the temperature intensity difference and the proportional adjustment weight are negatively correlated.

[0037] Preferably, the effectiveness determination unit includes:

[0038] The ratio calculation subunit is used to calculate the effective degree of the matching temperature range by using the degree of adjustment as the numerator and the product of the time delay and the residual as the denominator.

[0039] Preferably, the temperature control module includes:

[0040] The weight normalization unit is used to normalize the proportional adjustment weight for each matching temperature segment; the sum of the normalized proportional adjustment weights for all matching temperature segments is one.

[0041] The weighted summation unit is used to sum the proportional adjustment coefficients corresponding to the proportional adjustment action points of all matching temperature ranges using the normalized proportional adjustment weights as weights, so as to obtain the proportional adjustment coefficients for the current battery operating stage.

[0042] Preferably, the present invention also includes an intelligent temperature regulation and control system for new energy vehicle batteries, the system comprising the aforementioned battery temperature data acquisition module, historical temperature range matching module, temperature change key point analysis module, regulation matching degree analysis module, weight calculation module, and temperature control module.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] The historical temperature range matching module filters out matching temperature ranges from massive amounts of historical temperature data that correspond to the temperature intensity of the current battery operating stage, ensuring that temperature regulation is no longer divorced from actual operating conditions. Existing systems ignore the differences in battery temperature changes across different operating stages and employ a uniform adjustment logic. In contrast, this system, through historical matching, can find similar historical temperature change patterns based on the current operating conditions, providing a realistic reference for subsequent adjustments. This makes the adjustment strategy more targeted and avoids adjustment deviations caused by differences in operating conditions.

[0045] The temperature change key point analysis module analyzes the time-series temperature data changes within the matched temperature range, determines the proportional adjustment action point, and calculates the time delay, providing support for optimizing the adjustment timing. In battery temperature regulation, the timing of adjustment is crucial; adjusting too early or too late will affect the adjustment effect. By analyzing key nodes of temperature changes in historical matched temperature ranges, it clarifies when to initiate proportional adjustment under current operating conditions to achieve the best results. Simultaneously, the calculation of the time delay effectively avoids the problem of untimely adjustment caused by system response lag, ensuring that adjustment measures can be precisely intervened when temperature changes enter a critical stage.

[0046] The adjustment matching degree analysis module compares the changes in proportional adjustment data sequences and temperature data sequences before and after the proportional adjustment point of action to determine the adjustment matching degree for each matching temperature segment. This process enables a quantitative evaluation of historical adjustment effects. Existing systems struggle to determine the actual effectiveness of past adjustment strategies under similar operating conditions. However, this system, through adjustment matching degree analysis, can identify historical temperature segments with better adjustment effects, making subsequent weight allocation more rational and ensuring that the final adjustment strategy leans towards methods proven effective in practice, thus reducing ineffective adjustment behaviors.

[0047] The weighting calculation module combines time delay, adjustment matching degree, and the temperature intensity difference between the matching temperature range and the current battery operating stage to determine the proportional adjustment weight of the matching temperature range. This multi-factor comprehensive weighting calculation method makes the determination of the adjustment coefficient more scientific. Different historical temperature ranges have different reference values ​​for the current operating conditions. Some historical ranges have small time delays but average adjustment matching degrees, while others have high adjustment matching degrees but large temperature intensity differences. Weighting calculation can balance the influence of various factors, so that the adjustment experience of each matching temperature range can participate in the determination of the current adjustment coefficient according to its actual reference value, avoiding the one-sidedness of adjustment caused by a single factor.

[0048] The temperature control module weights the proportional adjustment coefficients of all matching temperature ranges to obtain the proportional adjustment coefficient for the current operating stage. This coefficient, combined with real-time temperature data, is input to the fuzzy controller to control the cooling device's operating power, further improving the adaptability and accuracy of the adjustment. The fuzzy controller possesses strong nonlinear processing capabilities, enabling it to handle complex situations during battery temperature changes. Even when the battery is in different operating stages and the temperature fluctuates or abruptly changes, the fuzzy controller can flexibly adjust the cooling device's operating power based on the input adjustment coefficient and real-time temperature data. This prevents both insufficient cooling leading to temperature runaway and overcooling causing energy waste, consistently maintaining the battery temperature within a suitable operating range. This ensures the battery always operates in optimal condition, extending its lifespan and stabilizing the vehicle's range and operational safety. Attached Figure Description

[0049] Figure 1 This is a timing diagram of the intelligent temperature regulation and control system for new energy vehicle batteries described in this invention.

[0050] Figure 2 Workflow diagram for the historical temperature range matching module;

[0051] Figure 3 This is a flowchart of the temperature fluctuation analysis submodule. Detailed Implementation

[0052] 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.

[0053] Please see Figure 1 The present invention provides an intelligent temperature regulation and control system for new energy vehicle batteries. The system includes: a battery temperature data acquisition module, a historical temperature range matching module, a temperature change key point analysis module, a regulation matching degree analysis module, a weight calculation module, and a temperature control module.

[0054] The battery temperature data acquisition module collects real-time temperature data from the new energy vehicle battery pack using temperature sensors. This data is stored and processed in time-series format. The historical temperature range matching module retrieves data from the stored historical temperature database and, based on the temperature characteristics of the current battery operating stage, selects historical temperature ranges that match the data. The temperature change key point analysis module performs time-series analysis on each matched temperature range, identifies points where significant temperature data changes as proportional adjustment action points, and calculates the time delay from the starting point to that point. The adjustment matching degree analysis module evaluates the changes in the data sequence before and after the proportional adjustment action point, determining the adjustment matching degree by comparing slope differences. The weight calculation module combines the time delay, adjustment matching degree, and temperature intensity difference to calculate the proportional adjustment weight for each matched temperature range. The temperature control module uses the weighted proportional adjustment coefficient and real-time temperature data to input into a fuzzy controller, adjusting the operating power of the battery cooling device to achieve intelligent temperature control.

[0055] Example 1: See Figure 2The modules work collaboratively to filter out temperature ranges from historical data that match the current battery operating conditions. The core of this module lies in locating potential effective historical intervals by analyzing temperature fluctuation characteristics and intensity calculations. The temperature fluctuation analysis submodule first processes the complete historical temperature data sequence, which typically originates from battery pack temperature information continuously recorded during vehicle operation. The processing begins with slope calculation; for each data point in the sequence, the instantaneous rate of change between it and the next data point, i.e., the slope value, is calculated. This value is obtained by quotienting the temperature difference and the time interval, thus quantifying the instantaneous temperature change trend. Subsequently, the segmentation unit scans the entire slope sequence to identify the first occurrence of the maximum value point, which often corresponds to the moment of most drastic temperature change and is marked as a key segmentation data point. The mean calculation unit then divides the entire historical temperature sequence into two parts using this segmentation point as the boundary and calculates the arithmetic mean of all slope values ​​within each part. By comparing the magnitudes of these two averages, the part with the larger mean is identified as the temperature-affected part. This part of the data usually reflects significant temperature fluctuations caused by external environmental or load changes and has higher analytical value.

[0056] The temperature intensity calculation submodule focuses on further processing the identified temperature-affected parts. This submodule divides the continuous temperature data into multiple fixed-length temperature data segments. Each segment contains the same number of sampling points, for example, ten consecutive sampling points constitute an analysis unit. For each data segment, the temperature intensity value is calculated. This intensity is characterized by the arithmetic mean of all temperature sampling values ​​within the segment, thus obtaining a scalar index that reflects the overall thermal state of the segment. The matching and filtering submodule undertakes the final filtering task. This submodule acquires the real-time temperature data of the current battery operating stage and calculates its temperature intensity value in the same way, that is, treating the current operating stage as a data segment and calculating the average temperature within this period. Then, this module compares the temperature intensity of each data segment in the historical temperature-affected part with the current intensity value, calculates the absolute difference between the two, and applies a negative correlation mapping function to convert the difference into a similarity index. This function usually adopts an exponential decay form, so that the smaller the difference, the closer the similarity is to one. Based on a preset similarity threshold, such as 0.8, all segments with similarity higher than the threshold are filtered from the historical data segments and marked as matching temperature segments. These matching segments represent the operating condition segments that are most similar to the current battery thermal state in history, and the proportional adjustment information contained therein will provide a reference for the current temperature control.

[0057] The temperature fluctuation analysis submodule ensures that the analysis focus is concentrated on the most sensitive intervals in historical data, avoiding the computational burden of full-sequence scanning. The temperature intensity calculation submodule transforms complex temperature sequences into concise intensity features through segmentation and mean calculation, simplifying the matching dimension. The matching filtering submodule achieves fast and accurate historical data retrieval through similarity calculation and threshold processing. The three submodules work together to complete the process of locating effective reference segments from massive historical data. This process does not require complex mathematical models or optimization algorithms, but can be achieved by relying on basic arithmetic operations and comparison judgments. It has the characteristics of high computational efficiency and good real-time performance, making it suitable for the resource-constrained environment of automotive embedded systems. The final output matching temperature segment will be used as basic data and sent to the subsequent key point analysis and weight calculation modules to further support the generation of proportional adjustment coefficients.

[0058] Example 2: See Figure 3 The module performs detailed processing of historical temperature data sequences to identify temperature ranges significantly affected by external factors. The slope calculation unit traverses the input temperature data sequence, calculating the rate of change between each data point and subsequent points. This rate of change is obtained by dividing the temperature difference by the time interval, forming a corresponding slope value sequence. The segmentation unit then scans the slope sequence to locate the first occurrence of the maximum value, which corresponds to the moment of most drastic temperature change and is marked as a key segmentation point. The mean calculation unit uses this segmentation point as a boundary to divide the original temperature sequence into a first subsequence and a second subsequence, and calculates the arithmetic mean of all slope values ​​within each subsequence. The affected portion determination unit compares the magnitude of these two averages, identifying the subsequence with the larger mean as the temperature-affected portion. This portion of data typically contains temperature fluctuations caused by sudden environmental changes or load variations, and has higher analytical value.

[0059] The implementation of the matching and filtering submodule focuses on filtering historical segments that match the current operating conditions from the temperature-affected portion. The similarity mapping unit of this submodule receives the divided temperature-affected portion, which is further subdivided into multiple continuous temperature data segments, each containing a fixed number of sampling points. The unit calculates the temperature intensity value of each historical data segment, i.e., the average value of the temperature sampling points within the segment, and simultaneously obtains the real-time temperature intensity of the current battery operating stage. By calculating the absolute difference between the historical intensity and the current intensity, and applying a negative exponential mapping function, the difference is converted into a similarity value. This function ensures that the smaller the difference, the closer the similarity is to one. The matching segment determination unit filters according to a preset similarity threshold (e.g., 0.8), marking all historical data segments with similarity higher than the threshold as matching temperature segments. The slope calculation unit considers the continuity of the data sequence and adopts a point-by-point sliding calculation method to avoid ignoring any potential change points. When identifying the maximum slope value, the partitioning unit selects the position of the first occurrence if there are multiple identical maximum values ​​to ensure consistent segmentation. When calculating the average slope of the subsequence, the mean calculation unit uses the arithmetic mean method, which is simple, effective, and computationally inefficient. The affected part determination unit makes a judgment by directly comparing the mean values, without the need for complex logic, making it suitable for embedded system implementation.

[0060] The negative exponential function used in the similarity mapping unit includes an adjustable parameter, preset according to battery type and application scenario, to control the sensitivity of similarity to differences. The threshold of the matching segment determination unit can be dynamically adjusted according to the system operating status; for example, the threshold can be appropriately lowered when temperature fluctuations are severe to obtain more matching segments and ensure system adaptability. The entire process requires no complex calculations or optimization algorithms, relying only on basic arithmetic and comparison operations, making it computationally efficient and resource-efficient. The final output set of matching temperature segments represents the operating condition segments that are historically closest to the current thermal state. The proportional adjustment information contained in these segments will provide data support for real-time control.

[0061] Taking a new energy vehicle driving in a high-temperature environment in summer as an example, the battery management system begins to perform temperature regulation tasks. The temperature data of the current battery operating stage shows a specific pattern: the initial temperature is stable at 32°C, and as the vehicle accelerates and the air conditioning load increases, the temperature rises to 38°C within 5 minutes and remains fluctuating. The system needs to find historical temperature ranges with similar operating conditions from the historical database as adjustment references. The historical temperature database stores temperature records of the vehicle's operation over the past three months. This data is stored in time series, including timestamps and corresponding battery pack temperature values. The temperature fluctuation analysis submodule calls the historical data sequence of the most recent 30 days, which contains approximately 100,000 data points with a sampling interval of 2 seconds. The slope calculation unit begins to traverse these data, calculating the rate of temperature change between every two adjacent data points. For example, if the temperature is recorded to change from 31.5°C to 32.1°C at a certain time point with a time interval of 2 seconds, the slope is (32.1-31.5) / 2=0.3°C / second. After scanning the entire sequence, a maximum slope of 1.2°C / second is identified at a certain historical time point, and this point is marked as a dividing data point.

[0062] The mean calculation unit divides the historical sequence into two parts based on the dividing point: the first part contains approximately 40,000 data points before the dividing point, and the second part contains approximately 60,000 data points after the dividing point. The average slope of each part is calculated: the average slope of the first part is 0.15°C / second, and the average slope of the second part is 0.08°C / second. The affected part determination unit compares these two averages and selects the first part with the larger average slope as the affected temperature part, reflecting the historically significant temperature fluctuations. The temperature intensity calculation submodule divides the selected affected temperature part into 500 consecutive data segments, each containing 80 data points (approximately 2.5 minutes of data). The temperature intensity value for each data segment is calculated as the arithmetic mean of the 80 temperature values ​​within that segment. For example, segment 123 contains temperature data from timestamp t1 to t80, and its intensity value is (31.8 + 32.0 + ... + 33.2) / 80 = 32.5°C.

[0063] The matching and filtering submodule begins its work, first calculating the temperature intensity of the current battery operating phase: the current phase lasts for 5 minutes, includes 150 temperature sampling points, and its average value is calculated to be 35.2°C; the similarity mapping unit traverses 500 data segments of the historical temperature-affected parts, calculates the absolute difference between the temperature intensity of each segment and the current intensity of 35.2°C, and applies the mapping function similarity = exp(-0.1*|Δ|), where Δ is the intensity difference; for example, if the intensity of a certain historical segment is 34.8°C, the difference |35.2-34.8|=0.4, and the similarity = exp(-0.1*0.4)=0.96. The matching segment determination unit sets a similarity threshold of 0.9 and filters out all segments with a similarity greater than this value from 500 historical segments. Finally, 15 matching temperature segments are found. The historical temperature intensity of these segments is between 34.6°C and 35.8°C, which is closest to the current operating conditions. The time distribution of these matching segments spans different dates, but they all show similar temperature characteristics to the current situation, including similar heating rates and fluctuation patterns.

[0064] Example 3: Identifying key change points from matched temperature segments and evaluating their adjustment effects. The abrupt change point identification unit of this module processes each matched temperature segment, which is a continuous temperature sequence selected from historical data. The unit adopts a rate-of-change detection method, which obtains the instantaneous rate of change by calculating the ratio of the temperature change between adjacent data points to the time interval. When the rate of change exceeds a preset threshold, such as five degrees Celsius per minute, the point is determined to be an abrupt change point and the first such point is marked as the proportional adjustment action point. This point marks the location where the temperature change trend changes significantly. The time delay is then calculated as the difference between the start time of the matched temperature segment and the time of the proportional adjustment action point. This value is stored in seconds and used to quantify the delay characteristics of the adjustment response.

[0065] The implementation of the adjustment matching degree analysis module aims to evaluate the degree of agreement between the proportional adjustment operation and the actual temperature change. The proportional adjustment difference calculation unit of this module processes the data sequence before the proportional adjustment point, which includes temperature values ​​and corresponding adjustment device operation records. The unit calculates the slope difference between adjacent data points in the sequence and takes the average of these differences to obtain the proportional adjustment difference value, which reflects the intensity of the adjustment fluctuation. The temperature difference calculation unit processes the temperature data sequence after the proportional adjustment point, using the same method to calculate the average of adjacent slope differences to obtain the temperature difference value, which characterizes the fluctuation of natural temperature changes. The matching degree mapping unit evaluates the adjustment effect by comparing these two difference values, and its calculation formula is as follows:

[0066]

[0067] in: This indicates adjusting the matching degree. Representative proportion adjusted difference value, Represents the temperature difference value. It is a small constant introduced to avoid division by zero; the formula ensures that the degree of matching approaches one when two difference values ​​are close, indicating that the adjustment operation is highly synchronized with the temperature change.

[0068] The mutation point identification unit uses a sliding window averaging method to smooth data fluctuations when calculating the rate of change. The window size is set according to the sampling frequency, and the threshold is dynamically adjusted according to the statistical characteristics of historical data to adapt to different operating conditions. The proportional adjustment difference calculation unit first normalizes the original records to eliminate the influence of dimensions when processing adjustment operation data, ensuring that the slope difference calculation is based on a unified standard. The temperature difference calculation unit also preprocesses the temperature data, using low-pass filtering to remove high-frequency noise and ensure the stability of subsequent calculations. The ε value in the matching degree mapping unit is usually set to a very small positive number, such as 1e-5, and its existence is only for numerical stability without affecting the calculation logic. The entire calculation process avoids complex optimization algorithms, relying only on basic arithmetic operations and comparison operations, exhibiting high computational efficiency and low resource consumption, making it suitable for real-time operation in automotive embedded systems. The final output time delay and adjustment matching degree provide a quantitative basis for subsequent weight calculations. These parameters together characterize the timeliness and effectiveness of historical adjustment behavior.

[0069] Suppose that the battery temperature of an electric vehicle is rapidly increasing during high-speed driving. The current battery operating temperature rises from 28°C to 41°C within the first ten minutes. The system selects three matching temperature segments from the historical database as analysis objects. These segments come from different dates but have similar temperature rise characteristics. The temperature change key point analysis module begins processing the first matching temperature segment (segment A), which contains 120 continuous temperature sampling points over a time span of 4 minutes. The abrupt change point identification unit uses a sliding window algorithm to calculate the instantaneous rate of change at each point. The window width is set to 5 data points. When the rate of change at the 47th data point reaches 0.8°C / second and exceeds the preset threshold of 0.7°C / second, this point is identified as the proportional adjustment point. The time delay calculation unit then calculates the time difference from the segment's starting point to this point. The sampling interval for this segment is 2 seconds, therefore the time delay is 46 × 2 = 92 seconds.

[0070] The adjustment matching degree analysis module then analyzes segment A. The proportional adjustment difference calculation unit extracts the proportional adjustment data sequence corresponding to the 46 data points before the point of action (this sequence records the historical cooling power adjustment values) and calculates the average value of the slope difference corresponding to adjacent adjustment values. In the specific calculation, the slope change of each adjacent point is first obtained, and then the arithmetic mean is calculated after taking the absolute value of these changes, resulting in a proportional adjustment difference value of 0.15. The temperature difference calculation unit processes the 74 temperature data points after the point of action and calculates the average value of the slope difference between adjacent temperatures using the same method, resulting in a temperature difference value of 0.18. The matching degree mapping unit converts the absolute difference between the two difference values ​​into the adjustment matching degree through negative correlation mapping. The calculation formula is 1-|0.15-0.18| / max(0.15,0.18)=1-0.03 / 0.18≈0.83. Analysis of the second matched temperature segment (segment B) shows that its proportional adjustment point of action is located at the 38th data point, with a time delay of 76 seconds; the proportional adjustment difference value of this segment is 0.12, and the temperature difference value is 0.14, so the adjustment matching degree is calculated as 1 - |0.12 - 0.14| / 0.14 ≈ 0.86; the point of action of the third matched temperature segment (segment C) is located at the 52nd data point, with a time delay of 104 seconds, a proportional adjustment difference value of 0.19, and a temperature difference value of 0.21, so the adjustment matching degree is calculated as 1 - |0.19 - 0.21| / 0.21 ≈ 0.90.

[0071] The entire analysis process emphasizes the continuity of data processing. A dynamic threshold adjustment mechanism is used for abrupt change point identification, automatically lowering the rate of change threshold to improve detection sensitivity when the ambient temperature is high. Triple moving average filtering is used to eliminate measurement noise interference during slope difference calculation. The maximum value function in the matching degree calculation ensures that the denominator always has a positive value, avoiding calculation errors. The module outputs time delays of 92 seconds, 76 seconds, and 104 seconds for the three matching segments, with matching degrees of 0.83, 0.86, and 0.90, respectively. These parameters provide a quantitative basis for subsequent weight calculations. The system completes this analysis process in approximately 350 milliseconds, fully meeting real-time control requirements. The analysis results accurately reflect the response speed and control effect characteristics of different historical adjustment behaviors. Through comparative analysis of multiple historical data segments, the system can identify the historical operating condition with the fastest response and most synchronized adjustment as the main reference object for current control.

[0072] Example 4: The module's coordinate point determination unit first processes each matching temperature segment. Each segment contains proportional adjustment action point information extracted from historical data. The unit uses the proportional adjustment coefficient of that point as the abscissa value. This coefficient is usually a value between zero and one, representing the historical adjustment intensity of the cooling device. At the same time, it uses the corresponding time delay as the ordinate value. This value is in seconds and represents the time difference from the start of the temperature segment to the adjustment action point. Thus, a two-dimensional coordinate point is generated for each matching temperature segment. The curve fitting unit collects the coordinate points of all matching segments and uses the least squares method to perform linear regression analysis to generate a fitting straight line that best represents the distribution trend of these points. This straight line reflects the statistical relationship between the proportional adjustment coefficient and the time delay.

[0073] The residual acquisition unit calculates the vertical distance from each coordinate point to the fitted line as the residual magnitude, which represents the degree of deviation between the actual point and the ideal fitted model. The effectiveness determination unit combines the time delay, adjustment matching degree, and residual magnitude to calculate the effectiveness value of each matching segment. The calculation method is to divide the adjustment matching degree by the product of the time delay and the residual magnitude. This calculation reflects the comprehensive relationship between the adjustment effect, response speed, and data stability. Finally, the weight determination unit calculates the initial weights based on the effectiveness and temperature intensity difference. The temperature intensity difference is the absolute difference between the temperature intensity of the current battery operating stage and the temperature intensity of the historical matching segment. The difference value is scaled by an exponential decay function and multiplied by the effectiveness to obtain the initial weight. All initial weights are normalized to sum to one, and finally, the proportional adjustment weight of each matching temperature segment is obtained.

[0074] To illustrate this process, assume that the system selects three matching temperature ranges when processing the current battery operating phase. For relevant data, please refer to Table 1.

[0075] Table 1: Calculation data for weighting matching temperature segments

[0076]

[0077] The coordinate point determination unit generates coordinate points for each segment: segment A (0.65, 12.3), segment B (0.72, 8.7), and segment C (0.58, 15.1). The curve fitting unit performs linear fitting on these points to obtain the equation of the fitted line. The residual acquisition unit calculates the distance from each point to the line, assuming the residual values ​​are as shown in the table. The effectiveness determination unit calculates the effectiveness of segment A as 0.85 / (12.3×0.12)=0.576, segment B as 0.92 / (8.7×0.08)=1.322, and segment C as 0.78 / (15.1). 1×0.15)=0.344; The weight determination unit takes the adjustment parameter c=0.05, and calculates the initial weight of segment A as 0.576×e^(-0.05×1.2)=0.542, segment B as 1.322×e^(-0.05×0.8)=1.271, and segment C as 0.344×e^(-0.05×2.1)=0.310; After normalization, the sum is 0.542+1.271+0.310=2.123, and the final weights are segment A: 0.255, segment B: 0.598, and segment C: 0.146. The coordinate point determination unit ensures data accuracy, with the proportional adjustment coefficient retained to two decimal places and the time delay retained to one decimal place; the curve fitting unit uses a linear model to balance computational complexity and fitting accuracy; the residual acquisition unit uses a precise geometric distance calculation formula; the effectiveness determination unit adds a very small constant to the denominator to prevent division by zero errors; the exponential function in the weight determination unit effectively reduces the influence weight of historical periods with large temperature differences, making the system more inclined to select historical data that is closer to the current operating conditions; normalization processing ensures that the sum of all weights is one, satisfying the mathematical requirements of weighted summation.

[0078] Example 5: The weighted summation unit then obtains the proportional adjustment coefficient corresponding to each matching temperature segment. These coefficients are the actual proportional adjustment values ​​applied at the point of application of proportional adjustment extracted from historical data. The unit multiplies each normalized weight by the corresponding proportional adjustment coefficient, and then adds all the product results to obtain the final weighted average proportional adjustment coefficient. This coefficient integrates the adjustment experience of multiple historical matching segments and reflects the most suitable proportional adjustment intensity for the current operating condition. In specific implementation, it is assumed that the system selects three matching temperature segments when processing the current battery operating stage, and their initial weights and proportional adjustment coefficients are as follows: the initial weight of matching segment A is 0.32, and the proportional adjustment coefficient is 0.65; the initial weight of matching segment B is 0.48, and the proportional adjustment coefficient is 0.72; the initial weight of matching segment C is 0.25, and the proportional adjustment coefficient is 0.58. The weight normalization unit first calculates the initial total weight as 0.32 + 0.48 + 0.25 = 1.05, then divides each weight by this total: normalized weight for segment A = 0.32 / 1.05 ≈ 0.305, normalized weight for segment B = 0.48 / 1.05 ≈ 0.457, and normalized weight for segment C = 0.25 / 1.05 ≈ 0.238. The weighted summation unit then calculates the weighted ratio adjustment coefficient = 0.305 × 0.65 + 0.457 × 0.72 + 0.238 × 0.58 ≈ 0.198 + 0.329 + 0.138 = 0.665.

[0079] The obtained weighted proportional adjustment coefficient of 0.665 is input into the fuzzy controller along with the real-time acquired battery temperature data. The fuzzy controller uses a preset membership function and fuzzy rule base for processing. The input variables include the deviation between the current temperature value and the target temperature, as well as the rate of temperature change. The output variable is the cooling device power adjustment signal. The controller first fuzzifies the precise input values ​​into linguistic variables, such as "temperature too high" or "rate of change too large," then applies fuzzy rules for inference, and finally defuzzifies the inference result into a precise output signal. This signal adjusts the working power of the cooling device through the drive circuit, achieving precise regulation of the battery temperature. Weight normalization uses simple arithmetic operations to avoid complex mathematical processing, and the weighted summation process is direct and clear. The design of the fuzzy controller balances response speed and control accuracy. The module output directly affects the cooling device power adjustment, forming a closed-loop control loop, enabling the system to dynamically adjust the cooling intensity based on real-time temperature conditions and historical adjustment experience, maintaining the battery pack operating within the optimal temperature range.

[0080] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0081] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart temperature regulation and control system for new energy vehicle batteries, characterized in that, The system includes: The battery temperature data acquisition module is used to acquire real-time temperature data of the battery pack of new energy vehicles. The historical temperature range matching module is used to filter out matching temperature ranges from historical temperature data that match the temperature intensity of the current battery operating stage. The temperature change key point analysis module is used to analyze the temperature data changes of the matched temperature segment over time, determine the proportional adjustment action point of the matched temperature segment, and use the time difference between the starting point of each matched temperature segment and the proportional adjustment action point as the time delay of each matched temperature segment. The adjustment matching degree analysis module is used to compare the changes in the proportional adjustment data sequence before the proportional adjustment action point with the temperature data sequence after the proportional adjustment action point to determine the adjustment matching degree of each matching temperature segment. The weight calculation module is used to determine the proportional adjustment weight of the matching temperature range by combining the time delay, the degree of adjustment, and the temperature intensity difference between the matching temperature range and the current battery operating stage. The temperature control module is used to weight the proportional adjustment coefficients of all matching temperature ranges corresponding to the current battery operating stage by proportional adjustment weights, and determine the proportional adjustment coefficient of the current battery operating stage; and input the proportional adjustment coefficient and real-time temperature data into the fuzzy controller, which controls the operating power of the battery cooling device.

2. The intelligent temperature regulation and control system for new energy vehicle batteries according to claim 1, characterized in that, The historical temperature range matching module includes: The temperature fluctuation analysis submodule is used to identify the affected parts of the temperature from historical temperature data sequences based on battery temperature fluctuations. The temperature intensity calculation submodule is used to divide the historical temperature data sequence into multiple temperature data segments and determine the temperature intensity of each temperature data segment. The matching and filtering submodule is used to filter out matching temperature ranges from the temperature-affected portions of historical temperature data that match the temperature intensity of the current battery operating phase.

3. The intelligent temperature regulation and control system for new energy vehicle batteries according to claim 2, characterized in that, The temperature fluctuation analysis submodule includes: The slope calculation unit is used to obtain the slope value corresponding to each temperature data in the historical temperature data sequence. The division unit is used to divide the data based on the temperature data where the maximum slope value first appears. The mean calculation unit is used to divide the temperature data sequence into a first part and a second part by dividing the data into two parts, and to calculate the mean slope of the first part and the second part of the temperature data sequence. The affected part determination unit is used to identify the part with the largest average slope between the first and second parts as the temperature-affected part.

4. The intelligent temperature regulation and control system for new energy vehicle batteries according to claim 2, characterized in that, The matching and filtering submodule includes: The similarity mapping unit is used to negatively correlate each temperature data segment of the temperature-affected part of the historical temperature data with the temperature intensity difference of the current battery operating stage to obtain the temperature intensity similarity. The matching segment determination unit is used to determine, based on the temperature intensity similarity, a temperature data segment of the affected part of the historical temperature data that matches the current battery operating stage, as the matching temperature segment.

5. The intelligent temperature regulation and control system for new energy vehicle batteries according to claim 1, characterized in that, The key point analysis module for temperature changes includes: The mutation point identification unit is used to identify the point where the temperature data of the matching temperature range changes abruptly for any matching temperature range corresponding to the current battery operating stage, and to use the point where the proportional adjustment of the matching temperature range occurs.

6. The intelligent temperature regulation and control system for new energy vehicle batteries according to claim 1, characterized in that, The adjustment matching degree analysis module includes: The proportional adjustment difference calculation unit is used to calculate the average value of the difference between the slope values ​​of two adjacent data in the proportional adjustment data sequence before the proportional adjustment action point for each matching temperature segment, and use the result value as the proportional adjustment difference. The temperature difference calculation unit is used to calculate the average value of the difference between the slope values ​​of two adjacent data points in the temperature data sequence after the proportional adjustment point, and use the result value as the temperature difference. The matching degree mapping unit is used to perform a negative correlation mapping on the difference between the proportional adjustment difference and the temperature difference, and use the result value as the adjustment matching degree of the matching temperature range.

7. The intelligent temperature regulation and control system for new energy vehicle batteries according to claim 2, characterized in that, The weight calculation module includes: The coordinate point determination unit is used to determine the coordinate point corresponding to each matching temperature segment by using the proportional adjustment coefficient of the proportional adjustment action point of each matching temperature segment as the abscissa and the time delay of each matching temperature segment as the ordinate. The curve fitting unit is used to perform curve fitting on the coordinate points of all matching temperature ranges corresponding to the current battery operating stage, and obtain the fitted curve segment. The residual acquisition unit is used to obtain the residual between the coordinate points of each matched temperature segment and the fitted curve segment; An effectiveness determination unit is used to determine the effectiveness of each matching temperature range by combining the time delay, the adjustment matching degree, and the residual size. The weighting determination unit is used to determine the proportional adjustment weight of the matching temperature range based on the effectiveness level and the temperature intensity difference between the matching temperature range and the current battery operating stage; wherein, the effectiveness level and the proportional adjustment weight are positively correlated; and the temperature intensity difference and the proportional adjustment weight are negatively correlated.

8. The intelligent temperature regulation and control system for new energy vehicle batteries according to claim 7, characterized in that, The effectiveness determination unit includes: The ratio calculation subunit is used to calculate the effective degree of the matching temperature range by using the degree of adjustment as the numerator and the product of the time delay and the residual as the denominator.

9. The intelligent temperature regulation and control system for new energy vehicle batteries according to claim 1, characterized in that, The temperature control module includes: The weight normalization unit is used to normalize the proportional adjustment weight for each matching temperature segment; the sum of the normalized proportional adjustment weights for all matching temperature segments is one. The weighted summation unit is used to sum the proportional adjustment coefficients corresponding to the proportional adjustment action points of all matching temperature ranges using the normalized proportional adjustment weights as weights, so as to obtain the proportional adjustment coefficients for the current battery operating stage.

10. A smart temperature regulation and control system for new energy vehicle batteries, characterized in that, The system includes a battery temperature data acquisition module, a historical temperature range matching module, a temperature change key point analysis module, an adjustment matching degree analysis module, a weight calculation module, and a temperature control module as described in any one of claims 1 to 9.

Images