Vehicle-mounted direct-current power supply charging and discharging control method and system based on temperature monitoring

Abstract

The application relates to the technical field of power supply control, and discloses a vehicle-mounted direct-current power supply charging and discharging control method and system based on temperature monitoring. The method collects the internal multi-point temperature of the vehicle-mounted direct-current power supply and the vehicle interior environment temperature, aggregates the multi-point temperature and performs spatial correction to obtain an equivalent temperature value; the equivalent temperature is stored in a time sequence array for linear fitting to solve a temperature change rate; a temperature change acceleration is obtained by subtracting the current temperature change rate from the historical temperature change rate, and an environment correction coefficient is obtained by looking up a table in combination with the equivalent temperature and the environment temperature; a predicted temperature is calculated by using the temperature change rate and the equivalent temperature, a basic power is obtained through interpolation weighting, and a corrected power value is obtained in combination with the environment correction coefficient; the maximum allowable current is obtained by querying a safety boundary according to the temperature change acceleration, the upper limit of the power is calculated, and the target power is determined by comparison. The application realizes continuous dynamic power regulation and control, eliminates power jump and lag, suppresses heat accumulation, and improves the operation stability and safety of the power supply.

Description

[0001] This application relates to the field of power control technology, and more specifically, to a method and system for controlling the charging and discharging of an on-board DC power supply based on temperature monitoring. Background Technology

[0002] Temperature and charge / discharge power linkage control is a key technology in vehicle DC power supply management, and it is widely used in the operation and regulation of vehicle energy storage power supplies and vehicle backup DC power supplies. In practical applications, when the power supply is operating at high charge / discharge rates or in high-temperature environments, the power regulation response becomes sluggish after an abnormal temperature rise, which can easily lead to overheating damage to the power supply.

[0003] Current vehicle power supply power regulation algorithms mostly adopt segmented fixed value adjustment mechanism. This algorithm has clear logic and high execution efficiency, which meets the real-time control requirements of vehicle equipment. However, it only sets discrete power levels with fixed temperature ranges and does not establish a continuous dynamic mapping relationship between temperature and power. When the temperature changes, the power adjustment is a step jump and there is lag, which leads to serious heat accumulation in the power supply and cannot guarantee the long-term stable charging and discharging operation of the vehicle DC power supply. Summary of the Invention

[0004] The main purpose of this application is to provide a charging and discharging control method and system for vehicle DC power supply based on temperature monitoring, which aims to solve the technical problems of segmented setpoint power regulation of vehicle DC power supply, power step jump when temperature changes, regulation lag causing heat accumulation, and power supply being prone to overheating damage.

[0005] The first aspect of this application proposes a method for controlling the charging and discharging of an on-board DC power supply based on temperature monitoring, comprising: The system acquires multiple temperature values ​​inside the vehicle's DC power supply and the ambient temperature inside the vehicle. It then aggregates these multiple temperature values ​​and combines them with spatial correction processing to obtain an equivalent temperature value. The equivalent temperature value is stored in a time series array and linear fitting is performed to obtain the temperature change rate. The temperature change acceleration is obtained by subtracting the temperature change rate from the historical temperature change rate. The environmental correction coefficient is obtained by querying the preset gear table based on the equivalent temperature value and the in-vehicle ambient temperature value. The predicted temperature is calculated using the temperature change rate and equivalent temperature value, interpolated and weighted to obtain the base power, and multiplied by the environmental correction coefficient to obtain the corrected power value. The maximum allowable current is obtained by querying the safety boundary based on the acceleration due to temperature change. The upper limit of power is then calculated, and the smaller value is selected as the target power.

[0006] Furthermore, the step of obtaining multiple temperature values ​​inside the vehicle's DC power supply and the ambient temperature inside the vehicle, aggregating the multiple temperature values ​​and combining them with spatial correction processing to obtain an equivalent temperature value includes: Acquire multiple temperature values ​​inside the vehicle's DC power supply and the ambient temperature inside the vehicle; The internal multi-point temperature values ​​are divided into multiple regional temperature subarrays. Outlier removal processing is performed on each regional temperature subarray to obtain a regional robust array. The representative temperature of each region is obtained by performing aggregation operation on the robust array of each region, and the heat source temperature and the average temperature of the non-heat source region are determined based on the difference of the representative temperature of each region. Based on the dispersion of internal multi-point temperature values, a spatial correction coefficient is queried. The difference between the heat source temperature and the average temperature of the non-heat source area is combined with the spatial correction coefficient to obtain an adaptive correction increment. The adaptive correction increment is superimposed on the heat source temperature, and then temperature limiting processing is performed to obtain the equivalent temperature value.

[0007] Further, the step of storing the equivalent temperature value into a time series array and performing linear fitting to obtain the temperature change rate includes: Write the equivalent temperature value into the main time series array and the auxiliary time series array to obtain the updated main time series array and auxiliary time series array; Read the temperature data in the updated main time series array, perform extreme value removal and mean calculation on the temperature data, and obtain the truncated average temperature value. Obtain the covariance data and gain vector of the previous period, and perform recursive least squares operation based on the truncated average temperature value to obtain the estimated temperature change rate. The temperature data in the updated auxiliary time series array is read and fitted to obtain the slope value of the auxiliary window. The estimated temperature change rate is then fused with the slope value of the auxiliary window to obtain the temperature change rate.

[0008] Further, the step of obtaining the temperature change acceleration by subtracting the temperature change rate from the historical temperature change rate, and obtaining the environmental correction coefficient by querying a preset gear table based on the equivalent temperature value and the in-vehicle ambient temperature value, includes: Obtain the temperature change rate and the historical temperature change rate, perform difference calculation on the two and apply amplitude limiting to obtain the temperature change acceleration. The equivalent temperature value and the in-vehicle ambient temperature value are obtained to perform temperature difference calculation. The average ambient temperature difference is obtained by combining the historical ambient temperature difference. Based on the equivalent temperature value and the average ambient temperature difference, the preset gear table is consulted to obtain the candidate gear number; The candidate gear number is compared with the historical gear benchmark, and the basic correction coefficient obtained from the table is weighted to obtain the environmental correction coefficient.

[0009] Further, the step of obtaining the temperature change rate and the historical temperature change rate, performing difference calculation on the two and limiting the amplitude to obtain the temperature change acceleration includes: Obtain the temperature change rate and historical temperature change rate, perform difference calculation to obtain the original acceleration value, and merge the original acceleration value with the historical acceleration data to obtain a filtered data group; Read the filtered data set, perform a fusion filtering process combining the median and the mean, and obtain the filtered acceleration value; The filtered acceleration value is compressed according to a preset compression boundary to obtain a compression acceleration value; The compression acceleration value is compared with the dynamic limiting threshold and a limiting process is performed to obtain the temperature change acceleration.

[0010] Further, the step of calculating the predicted temperature using the temperature change rate and equivalent temperature value, interpolating and weighting to obtain the base power, and multiplying it by the environmental correction coefficient to obtain the corrected power value includes: A two-way prediction calculation is performed based on the temperature change rate and the equivalent temperature value, and a conservative prediction temperature is determined by combining the results of the mapping table lookup. Using the conservatively predicted temperature as an index, the power-temperature mapping table is queried, the power data of adjacent temperature ranges is read, and the boundary distance information is calculated. Based on the boundary distance, the transition zone condition is determined, and the power data of adjacent temperature ranges are interpolated or weighted and mixed to obtain the base power value; Obtain the environmental correction factor, and multiply the base power value with the environmental correction factor to obtain the corrected power value.

[0011] Further, the step of querying the power-temperature mapping table using the conservatively predicted temperature as an index, reading the power data of adjacent temperature ranges, and calculating the boundary distance information includes: The conservative predicted temperature and the coarse power temperature mapping table are compared and decoded to obtain the coarse division level number. Based on the coarse subdivision number, a target subdivision mapping table is selected, and an offset conversion process is performed on the conservative predicted temperature to obtain the truncated index value. Access the target subdivision mapping table based on the truncated index value, and read the temperature boundary value and power value corresponding to the adjacent gear. The boundary distance information is obtained by performing a difference operation between the conservatively predicted temperature and the temperature boundary value.

[0012] Furthermore, the step of obtaining the maximum allowable current based on the temperature change acceleration query safety boundary, calculating the power upper limit, and selecting the smaller value as the target power includes: The acceleration values ​​of temperature change are obtained and written into a sliding array. Extreme value removal and mean operation are performed on the acceleration data in the array to obtain the truncated and filtered acceleration values. Based on the truncated filter acceleration value, the safety boundary curve table is consulted, and interpolation is performed to obtain the spline interpolated current value; A two-dimensional query coordinate system is constructed using the truncated filter acceleration value and the equivalent temperature value. The safety boundary surface is queried and interpolation is performed to obtain the bilinear interpolated current value. The maximum allowable charge / discharge current value is obtained by performing a fusion operation on the spline interpolation current value and the bilinear interpolation current value. The maximum allowable charge / discharge current value is then multiplied by the battery terminal voltage to obtain the upper limit of power. The upper limit power value and the corrected power value are compared numerically to select the smaller value to obtain the safe power. The rate of change constraint processing is applied to the safe power to obtain the target power.

[0013] Further, the step of querying the safety boundary curve table based on the truncated filter acceleration value, performing interpolation calculations, and obtaining the spline interpolated current value includes: The truncated filtered acceleration value is compared and decoded with the node values ​​in the safety boundary curve table to obtain the original lower node index, and the historical index reference is read. Perform backlash stabilization processing on the original lower node index and the historical index benchmark to obtain a stable lower node index and obtain the boundary condition flag. Based on the stable lower node index, read the node data in the safety boundary curve table, select the interpolation mode and construct a quadratic spline polynomial by combining the boundary condition flags; Substituting the truncated filtered acceleration value into the quadratic spline polynomial, and combining it with historical current data to perform fusion and constraint processing, the spline interpolated current value is obtained.

[0014] A second aspect of this application also proposes a vehicle-mounted DC power supply charging and discharging control system based on temperature monitoring, comprising: The equivalent temperature calculation module is used to obtain the temperature values ​​of multiple points inside the vehicle DC power supply and the temperature value of the vehicle interior environment. It aggregates the temperature values ​​of multiple points and combines them with spatial correction processing to obtain the equivalent temperature value. The temperature change rate calculation module is used to store the equivalent temperature value into a time series array and perform linear fitting to obtain the temperature change rate; The environmental correction coefficient acquisition module is used to obtain the temperature change acceleration by subtracting the temperature change rate from the historical temperature change rate, and to obtain the environmental correction coefficient by querying a preset gear table based on the equivalent temperature value and the in-vehicle ambient temperature value. The corrected power calculation module is used to calculate the predicted temperature using the temperature change rate and equivalent temperature value, interpolate and weight the base power, and multiply it by the environmental correction coefficient to obtain the corrected power value. The target power determination module is used to query the safety boundary based on the temperature change acceleration to obtain the maximum allowable current, calculate the upper limit of power, and select the smaller value as the target power.

[0015] The first aspect of this plan brings the following benefits: This application obtains the equivalent temperature by collecting the temperature of multiple points of the power supply and combining it with spatial correction. It accurately obtains the temperature change rate and acceleration by relying on time series fitting, and obtains the dynamic environmental correction coefficient by looking up environmental parameters. At the same time, it constructs a continuous power mapping relationship by temperature prediction interpolation weighting, and outputs the target power by combining it with safety boundary constraints. It abandons the traditional fixed segmented setpoint control method, eliminates the power step jump and regulation lag problem under temperature fluctuation, effectively suppresses power heat accumulation, avoids the risk of overheating damage, and greatly improves the stability and safety of vehicle DC power supply charging and discharging operation. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of a vehicle-mounted DC power supply charging and discharging control method based on temperature monitoring according to an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a vehicle-mounted DC power supply charging and discharging control system based on temperature monitoring, according to an embodiment of this application. Figure 3 This is a schematic diagram of the structure of a computer device according to an embodiment of this application; The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] Those skilled in the art will understand that, unless explicitly stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in the specification of this application means the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is “connected” or “coupled” to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein may include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any modules and all combinations of one or more associated listed items.

[0019] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0020] Reference Figure 1 This application provides a method for controlling the charging and discharging of an on-board DC power supply based on temperature monitoring, comprising: S1: Obtain the temperature values ​​of multiple points inside the vehicle DC power supply and the temperature value of the vehicle interior environment. Aggregate the temperature values ​​of multiple points and combine them with spatial correction processing to obtain the equivalent temperature value. S2: Store the equivalent temperature value into a time series array and perform linear fitting to obtain the temperature change rate; S3: The temperature change acceleration is obtained by subtracting the temperature change rate from the historical temperature change rate. Based on the equivalent temperature value and the in-vehicle ambient temperature value, the environmental correction coefficient is obtained by querying the preset gear table. S4: Calculate the predicted temperature using the temperature change rate and equivalent temperature value, interpolate and weight it to obtain the base power, and multiply it by the environmental correction coefficient to obtain the corrected power value; S5: Based on the acceleration due to temperature change, query the safety boundary to obtain the maximum allowable current, calculate the upper limit of power, and select the smaller value as the target power.

[0021] In step S1, the reference acquisition object is first determined to be a vehicle-mounted backup DC power supply. The power supply has eight digital temperature sensors arranged inside, dividing it into three regions: a positive region, a negative region, and a module center region. During this sampling period, the internal temperatures at eight points are read as [62℃, 64℃, 63℃, 71℃, 62℃, 63℃, 61℃, 62℃]. The ambient temperature output by the vehicle interior temperature sensor is 45℃. 71℃ is the abnormal temperature jump point. The temperature subarrays for the positive region are [62, 64, 63], the negative region are [71, 62, 63], and the module center region is [61, 62]. Next, prepare the input parameters for the equivalent temperature calculation: an internal multi-point temperature set (8 temperature samples, divided into 3 regional temperature subarrays); the vehicle interior ambient temperature (e.g., 45℃ for this sample); robust boundary parameters: median and three times the absolute median difference; the spatial correction coefficient is obtained from a table as the eight-point temperature dispersion level, which is 2 for this sample, resulting in a spatial correction coefficient β=0.8; and the hardware-stored absolute temperature upper limit threshold of 85℃. Then, perform regional outlier removal, regional representative temperature calculation, heat source identification, adaptive correction incremental calculation, and amplitude limiting calculation. The negative electrode region subarray [71,62,63] was filtered for outliers (71℃) by applying the median plus three times the absolute median difference, resulting in a robust negative electrode region subarray [62,63]. The representative temperatures of the three regions were calculated: positive electrode region 63℃, negative electrode region 62.5℃, and module center region 61.5℃. The temperature difference between adjacent regions was calculated, with the largest positive difference corresponding to the positive electrode region as the heat source-dominant region, with a heat source temperature of 63℃. The average temperature of the non-heat source region was taken as the average of the negative electrode region and the module center region, which is 62℃. The adaptive correction increment was calculated as 0.8×(63-62)=0.8℃. The unlimited equivalent temperature was calculated as 63+0.8=63.8℃. The minimum value between 63.8℃ and the absolute temperature limit of 85℃ was used to obtain the limited equivalent temperature value of 63.8℃ (the equivalent temperature output for this sample is 63.8℃). Because this step uses robust statistical methods to eliminate sensor outliers and combines the temperature difference between heat sources and non-heat sources with spatial dispersion for spatial correction, i.e.: T eq =min(T 热源 +β×(T 热源 -T 非热源平均 ),T 上限 Therefore, the output equivalent temperature is no longer simply equal to the highest or average temperature. The equivalent temperature is used to characterize the actual thermal stress inside the power supply and can be used as the input source data for the subsequent time series fitting in step S2. Taking this vehicle-mounted backup DC power supply sample as an example, the highest temperature at the original eight points is 64℃, and the equivalent temperature calculated in step S1 is 63.8℃. This equivalent temperature will be input into step S2 for time series array storage and linear fitting calculation. Step S1 transforms the discrete multi-sensor raw temperature measurement data into a single quantifiable equivalent temperature through multi-point temperature aggregation and spatial correction processing, providing standardized input for the subsequent temperature change rate solution.

[0022] In step S2, the processing object is first determined to be the aforementioned vehicle-mounted backup DC power supply. The limited equivalent temperature of 63.8℃ output from S1 is reused, and the controller communication sampling period is 100ms. The main sliding time series array stores the equivalent temperature of the last 5 sampling periods, and the auxiliary sliding time series array stores the equivalent temperature of the last 3 sampling periods. The current main time series array has historical equivalent temperatures [61.2℃, 62.0℃, 62.7℃, 63.2℃]. Next, the input parameters for calculating the rate of change of temperature are prepared: the equivalent temperature value of the current period (e.g., 63.8℃ corresponding to this sample); the historical temperatures of the fifth-order main sliding time series array [61.2℃, 62.0℃, 62.7℃, 63.2℃]; the historical temperatures of the third-order auxiliary sliding time series array [62.0℃, 62.7℃]; the covariance data and gain vector of the previous period; and the sampling time interval of 100ms. Then, 63.8℃ is written into the main sliding time series array, and the array is updated to [61.2℃, 62.0℃, 62.7℃, 63.2℃, 63.8℃]. The five temperatures in the array are sorted in ascending order, and the maximum value of 63.8℃ and the minimum value of 61.2℃ are removed. The average of the remaining [62.0℃, 62.7℃, 63.2℃] is calculated to obtain the cutoff average temperature of 62.63℃. The covariance and gain vector of the previous period are read and recursive least squares operation is performed to obtain the estimated temperature change rate of 0.014℃ / ms. 63.8℃ is written into the secondary sliding time series array, and updated to [62.0℃, 62.7℃, 63.8℃]. Least squares fitting is performed to obtain the secondary window slope of 0.012℃ / ms. The arithmetic mean of the two is taken as 0.013℃ / ms, and divided by the sampling interval of 100ms to obtain the fused temperature change rate k = 0.13℃ / 100ms. Because this step uses a double sliding window fusion fitting, i.e., k=k est +k aux / 2, therefore, the output fused temperature change rate simultaneously references the main window recursive least squares and auxiliary window least squares results; the temperature change rate characterizes the rate of change of the equivalent temperature within a unit sampling period and serves as the input for the subsequent acceleration calculation in S3. Taking the vehicle-mounted backup DC power supply sample as an example, the fused temperature change rate k = 0.13℃ / 100ms is calculated, and this value is input into step S3 to solve for the temperature change acceleration. Step S2 uses time-series array storage and dual-window linear fitting to transform the discrete time-series equivalent temperature sequence into a quantifiable temperature change rate, providing a standardized input for the subsequent acceleration solution.

[0023] In step S3, the processing target is first determined to be the aforementioned vehicle-mounted backup DC power supply. The current cycle temperature change rate k = 0.13℃ / 100ms output from S2 is reused, and the historical temperature change rate k_prev = 0.08℃ / 100ms from the previous cycle is reused. The equivalent temperature output from S1 is 63.8℃, the vehicle interior temperature is 45℃, and the historical ambient temperature difference from the previous cycle is 18.2℃. Next, the input parameters for calculating the environmental correction coefficient are prepared: current temperature change rate 0.13℃ / 100ms; historical temperature change rate 0.08℃ / 100ms; equivalent temperature 63.8℃; vehicle interior temperature 45℃; historical ambient temperature difference from the previous cycle 18.2℃; acceleration positive saturation threshold 0.06℃ / 100ms. 2 Negative saturation threshold: -0.06℃ / 100ms 2 ; Positive temperature difference range table; Historical range reference number 2; Hysteresis comparison threshold. Then, subtract the current temperature change rate from the historical temperature change rate to obtain the raw acceleration value a_raw = 0.13-0.08 = 0.05℃ / 100ms 2 The raw acceleration value, along with the historical acceleration values ​​from the previous two cycles, forms a three-element filtering buffer. After median-mean fusion filtering, numerical compression, and dynamic limiting threshold comparison, the output limited acceleration a = 0.05℃ / 100ms is obtained. 2 The real-time temperature difference between the equivalent temperature and the ambient temperature is calculated as: 63.8 - 45 = 18.8℃. The arithmetic mean of the real-time temperature difference and the historical ambient temperature difference is taken to obtain the average ambient temperature difference = (18.8 + 18.2) / 2 = 18.5℃. Since the average ambient temperature difference is positive, a positive temperature difference level table is selected. The original temperature level number 3 is obtained by querying the temperature range positioning table using the equivalent temperature. A backlash comparison is performed between the original temperature level number and the historical level benchmark to obtain the stable temperature level number 3. Based on the stable temperature level number and the average ambient temperature difference, a candidate level number 3 is obtained by querying the table. A backlash comparison is performed between the candidate level number and the historical level benchmark number 2, and the basic environmental correction coefficient of 0.92 is read. Combined with the stable temperature level number for weighted correction, the final environmental correction coefficient λ = 0.94 is obtained. Since the acceleration of temperature change is generated by the difference in the rate of temperature change between adjacent periods, i.e., a = kk... prev The environmental correction factor is obtained by combining the equivalent temperature and the average ambient temperature difference through a table lookup and weighted by backscattering. Acceleration characterizes the rate of temperature change, while the environmental correction factor characterizes the power correction factor based on the vehicle's cooling conditions. Both serve as input parameters for S4. Taking this onboard backup DC power supply sample as an example, the output limited acceleration a = 0.05℃ / 100ms. 2The environmental correction factor λ = 0.94. The two numerical inputs in step S4 are used for predicting temperature and calculating corrected power. Step S3 transforms the raw data of temperature change rate and temperature-ambient temperature difference into quantifiable acceleration and environmental correction factor through difference calculation, filtering, gear lookup table and backlash comparison, providing standardized input for subsequent power calculation.

[0024] In step S4, the processing target is first determined to be the aforementioned vehicle-mounted backup DC power supply. The equivalent temperature output from S1 is 63.8℃, the temperature change rate from S2 is k=0.13℃ / 100ms, and the environmental coefficient from S3 is λ=0.94. The dynamic prediction duration is 300ms (obtained from a table). The power-temperature coarse mapping table and fine mapping table are pre-stored in memory. Next, the input parameters for the quasi-corrected power calculation are: equivalent temperature 63.8℃; temperature change rate 0.13℃ / 100ms; dynamic prediction duration 300ms; environmental correction coefficient 0.94; power-temperature coarse mapping table; target fine mapping table; transition band width 5℃; low-end temperature 60℃, high-end temperature 70℃, corresponding to a low-end power of 2200W and a high-end power of 1600W. Then, calculate the forward predicted temperature = 63.8 + 0.13 × 300 = 102.8℃; the reverse predicted temperature = 63.8 - 0.13 × 300 = 24.8℃; take the smaller of the two to obtain the conservative predicted temperature of 24.8℃. Input the conservative predicted temperature into the power-temperature coarse subdivision mapping table for comparison and decoding to obtain coarse subdivision level number 1, select the corresponding target subdivision mapping table; convert the temperature offset and truncation index, read the temperature boundary and power of adjacent levels; calculate the lower boundary distance 24.8 - 20 = 4.8℃, and the upper boundary distance 30 - 24.8 = 5.2℃. Determine that it does not fall into the transition zone, directly perform linear interpolation operation, and calculate the basic power value P0 = 2440W. Multiply the basic power value by the environmental correction factor, the corrected power value = 2440 × 0.94 = 2293.6W. Since the conservative predicted temperature takes the smaller value of the two-way prediction results, the corrected power is calculated as: P 修正 =P0*λ; The base power is obtained by interpolation from the mapping table. The environmental correction factor completes the power multiplier correction for heat dissipation conditions. The corrected power is used as the input parameter of S5. Taking this vehicle-mounted backup DC power supply sample as an example, the corrected power value of 2293.6W is calculated. This value is input into step S5 for power upper limit comparison.

[0025] In step S5, the processing target is first determined to be the aforementioned vehicle-mounted backup DC power supply, and the output of S3 is reused with a limited acceleration a = 0.05℃ / 100ms. 2The equivalent temperature of the S1 output is 63.8℃, and the corrected power value of the S4 output is 2293.6W; the current battery terminal voltage U=27.2V; the upper limit of the output power in the previous cycle was 2100W. Next, prepare the input parameters for the target power calculation: limiting acceleration 0.05℃ / 100ms. 2 Equivalent temperature 63.8℃; corrected power value 2293.6W; battery terminal voltage 27.2V; fifth-order acceleration sliding array; safety boundary curve table, safety boundary surface; first weighting coefficient 0.6, second weighting coefficient 0.4; power increase threshold 120W, power decrease threshold -120W; minimum allowable power 800W. Then, the limited acceleration is written into the fifth-order acceleration sliding array, and the maximum and minimum values ​​in the array are removed to obtain the truncated filtered acceleration of 0.048℃ / 100ms. 2 The truncated filter acceleration is looked up in the safety boundary curve table. After node decoding and hysteresis stabilization, a quadratic spline polynomial is constructed to calculate the spline interpolated current value of 94A. Using the truncated filter acceleration and equivalent temperature as two-dimensional coordinates, the safety boundary surface is looked up, and bilinear interpolation is performed to obtain a bilinear interpolated current of 91A. Weighted fusion yields the maximum allowable charge / discharge current I_max = 94 × 0.6 + 91 × 0.4 = 92.8A. The power limit P_limit is calculated as 92.8 × 27.2 = 2524.16W. The smaller value between the corrected power of 2293.6W and the power limit of 2524.16W is taken to obtain the safe power of 2293.6W. The power change is calculated as 2293.6 - 2100 = 193.6W, which exceeds the rising change threshold of 120W. Therefore, the power change is constrained to 120W. The historical power upper limit is superimposed to obtain a rate-of-change constraint power of 2220W. This, combined with the minimum allowable power of 800W, yields the larger value, resulting in the final target power of 2220W. Since the maximum allowable current is obtained by weighted fusion of spline interpolation current and bilinear interpolation current, i.e., I... max =I 样条 ×w1+I 双线性 ×w2; then apply power limiting and power change rate constraints, and output the target power to the power converter. Taking this vehicle-mounted backup DC power supply sample as an example, the final output target power is 2220W. Step S5 uses acceleration filtering and dual-mode interpolation to obtain the maximum allowable current, power comparison, and change rate constraints, and converts the corrected power, acceleration, and voltage raw data into a target power that can be directly output, completing the entire charging and discharging control calculation closed loop.

[0026] In one embodiment, the step of obtaining multiple temperature values ​​inside the vehicle's DC power supply and the ambient temperature inside the vehicle, aggregating the multiple temperature values ​​and combining them with spatial correction processing to obtain an equivalent temperature value includes: Acquire multiple temperature values ​​inside the vehicle's DC power supply and the ambient temperature inside the vehicle; The internal multi-point temperature values ​​are divided into multiple regional temperature subarrays. Outlier removal processing is performed on each regional temperature subarray to obtain a regional robust array. The representative temperature of each region is obtained by performing aggregation operation on the robust array of each region, and the heat source temperature and the average temperature of the non-heat source region are determined based on the difference of the representative temperature of each region. Based on the dispersion of internal multi-point temperature values, a spatial correction coefficient is queried. The difference between the heat source temperature and the average temperature of the non-heat source area is combined with the spatial correction coefficient to obtain an adaptive correction increment. The adaptive correction increment is superimposed on the heat source temperature, and then temperature limiting processing is performed to obtain the equivalent temperature value.

[0027] In this embodiment, the first step is to acquire the raw temperature data of the vehicle-mounted DC power supply. The temperature data is consistent with that of the same vehicle-mounted backup DC power supply in the previous embodiment. The raw data has been read and parsed via the SPI bus. Taking this vehicle-mounted backup DC power supply sample as an example, the raw temperature collected by the 8 digital temperature sensors inside the power supply is [62℃, 64℃, 63℃, 71℃, 62℃, 63℃, 61℃, 62℃], and the vehicle interior temperature sensor outputs an interior temperature of 45℃. For other vehicle-mounted DC power supply samples of the same model in the batch, the corresponding internal eight-point temperature and the interior temperature are collected respectively. All temperature data are stored one-to-one according to the device samples to ensure sample matching for subsequent partitioning operations. The next step is to obtain multiple temperature subarrays for the divided regions and perform outlier removal. The regions are divided according to their physical locations: positive region, negative region, and module center region. Outlier removal needs to be performed on each subarray for each region. Taking the three subarrays of the vehicle-mounted backup DC power supply sample as an example, the positive region temperature subarray [62℃, 64℃, 63℃] is constructed by using the median and three times the absolute median difference to build a robust boundary interval. There are no outliers within the interval, resulting in the robust positive region array [62℃, 64℃, 63℃]. The negative region temperature subarray [71℃, 62℃, 63℃] is defined as an outlier because 71℃ exceeds the robust boundary interval and is removed, resulting in the robust negative region array [62℃, 63℃]. The module center region temperature subarray [61℃, 62℃] has no outliers, resulting in the robust module center region array [61℃, 62℃]. The subarrays of other power supply samples are also processed in the same way to remove outliers, forming a complete set of robust regional arrays.

[0028] Next, the representative temperatures of each region and the temperatures of the heat source and non-heat source areas are calculated. According to the scheme settings, the representative temperature of each region is the arithmetic mean of the robust arrays. Taking the vehicle-mounted backup DC power supply sample as an example, the representative temperature of the positive pole area is (62+64+63) / 3=63℃, the representative temperature of the negative pole area is (62+63) / 2=62.5℃, and the representative temperature of the module center area is (61+62) / 2=61.5℃. The temperature difference between adjacent areas is calculated. The difference between the positive pole area and the negative pole area is 0.5℃, and the difference between the negative pole area and the module center area is 1℃. The high-end area corresponding to the largest positive difference is the positive pole area, and the heat source temperature is taken as the representative temperature of the positive pole area, which is 63℃. The non-heat source area includes the negative pole area and the module center area. The average temperature of the non-heat source area is (62.5+61.5) / 2=62℃, thus completing the calculation of the heat source and non-heat source temperatures. Next, adaptive correction increment calculation is performed. According to the scheme settings, the spatial correction coefficient is obtained by looking up a table based on the eight-point temperature dispersion level. The dispersion level is divided into 1-3 levels. Combined with the variance of the eight-point temperature of this sample, the dispersion level is calculated to be 2, and the spatial correction coefficient β=0.8 is obtained by looking up the table. Taking this vehicle-mounted backup DC power supply sample as an example, the basic calculation is carried out according to the formula adaptive correction increment = β × (heat source temperature - average temperature of non-heat source area): 0.8 × (63-62) = 0.8℃; if the sample has a working condition where the temperature difference between the heat source and non-heat source is negative, the adaptive correction increment output is negative, and it also participates in the subsequent superposition calculation.

[0029] Next, equivalent temperature limiting is performed. The memory pre-stores an absolute temperature upper limit threshold of 85℃. The heat source temperature is superimposed with an adaptive correction increment to obtain the unlimited equivalent temperature, which is then compared with the absolute temperature upper limit to select the minimum value. Taking the vehicle backup DC power supply sample as an example, the unlimited equivalent temperature = 63 + 0.8 = 63.8℃. Since 63.8℃ is less than 85℃, the limited equivalent temperature value is 63.8℃. If the calculated unlimited equivalent temperature of a sample is 88℃, which is greater than 85℃, the limited equivalent temperature is forced to be 85℃ to avoid temperature values ​​exceeding the limit. Finally, the complete output result is determined. The output equivalent temperature of the vehicle backup DC power supply sample is 63.8℃. The other power supply samples in the batch are processed according to the same process, and the equivalent temperature set corresponding to each device sample is finally obtained. This equivalent temperature set is directly used as the input data for the aforementioned S2 timing array. This embodiment avoids the problem of single-point temperature measurement distortion by eliminating outliers in different zones, identifying heat sources, and correcting spatial conditions. It outputs an equivalent temperature that closely matches the actual thermal stress of the power supply, thereby improving the reliability of the vehicle power supply's thermal control.

[0030] In one embodiment, the step of storing the equivalent temperature value into a time series array and performing linear fitting to obtain the rate of temperature change includes: Write the equivalent temperature value into the main time series array and the auxiliary time series array to obtain the updated main time series array and auxiliary time series array; Read the temperature data in the updated main time series array, perform extreme value removal and mean calculation on the temperature data, and obtain the truncated average temperature value. Obtain the covariance data and gain vector of the previous period, and perform recursive least squares operation based on the truncated average temperature value to obtain the estimated temperature change rate. The temperature data in the updated auxiliary time series array is read and fitted to obtain the slope value of the auxiliary window. The estimated temperature change rate is then fused with the slope value of the auxiliary window to obtain the temperature change rate.

[0031] In this embodiment, the first step is to acquire the input data for time series fitting. The equivalent temperature data reuses the calculation results of the same vehicle-mounted backup DC power supply from the previous embodiment. The equivalent temperature of 63.8℃ has been limited and output. Taking this vehicle-mounted backup DC power supply sample as an example, the historical equivalent temperatures of the fifth-order main time series array are [61.2℃, 62.0℃, 62.7℃, 63.2℃], and the historical equivalent temperatures of the third-order auxiliary time series array are [62.0℃, 62.7℃]. The controller communication sampling period is fixed at 100ms. Other vehicle-mounted DC power supply samples of the same model in the batch each carry the corresponding historical data of the main and auxiliary time series arrays and the equivalent temperature. All data are stored one-to-one according to the device samples to ensure the sample matching of subsequent time series fitting calculations. Next, the equivalent temperature is written into the main time series array and the auxiliary time series array, and the array is updated according to the array pointer rules: taking the vehicle-mounted backup DC power supply sample as an example, the current equivalent temperature of 63.8℃ is written into the fifth-order main time series array, discarding the oldest historical data, and the updated main time series array is [61.2℃, 62.0℃, 62.7℃, 63.2℃, 63.8℃]; according to the auxiliary window update interval condition, 63.8℃ is written into the third-order auxiliary time series array, and the updated auxiliary time series array is [62.0℃, 62.7℃, 63.8℃]; the main and auxiliary time series arrays of other power supply samples are also written and updated according to this logic, forming a set of updated time series arrays. Next, the data in the main time series array is read, and extreme value removal and mean calculation are performed to obtain the truncated average temperature value. Taking the vehicle-mounted backup DC power supply sample as an example, the five temperatures in the main time series array are read and sorted in ascending order to obtain [61.2℃, 62.0℃, 62.7℃, 63.2℃, 63.8℃]. The maximum value of 63.8℃ and the minimum value of 61.2℃ are removed, and the middle three temperature groups [62.0℃, 62.7℃, 63.2℃] are retained. The arithmetic mean is calculated to obtain the cutoff average temperature value of 62.63℃. If there are abnormal temperature jumps in the array, the extreme value removal step will also remove abnormally large and small sampling points.

[0032] Next, a recursive least squares operation is performed to estimate the rate of temperature change. The covariance data and gain vector from the previous period stored in the register are read, and the truncated average temperature of 62.63℃ is used as input to perform the recursive least squares operation, yielding an estimated rate of temperature change of 0.014℃ / ms. Then, three sets of temperature data from the updated auxiliary time series array are read, and least squares linear fitting is performed to obtain the auxiliary window slope value of 0.012℃ / ms. According to the scheme settings, the fusion calculation is performed, taking the arithmetic mean of the two values, and then applying the formula k... temp =(k est +k aux The basic calculation is completed by (0.014 + 0.012) / 2 = 0.013℃ / ms. Dividing this by the sampling period of 100ms yields a fused temperature change rate of 0.13℃ / 100ms. If the auxiliary window exhibits noise disturbances and an abnormal output slope, it still participates in the weighted fusion calculation. Finally, the output result is determined. The vehicle-mounted backup DC power supply sample outputs a fused temperature change rate of 0.13℃ / 100ms. The remaining power supply samples in the batch are processed according to the same procedure to obtain the temperature change rate set corresponding to each device. This set serves as the input for S3 to solve for temperature change acceleration. This embodiment suppresses temperature sampling noise through dual-window fusion fitting, outputting a stable temperature change rate, providing reliable time-series data support for subsequent acceleration calculations.

[0033] In one embodiment, the step of writing the equivalent temperature value into the main time series array and the auxiliary time series array to obtain the updated main time series array and auxiliary time series array includes: Perform validity screening on the equivalent temperature values ​​and generate time-stamped valid data records by combining them with historical backup data; Write the valid data record to the corresponding storage location of the main time series array to complete the data and pointer state update of the main time series array; The valid data records are processed according to the preset update conditions, and selectively written into the auxiliary time series array to complete the data update of the auxiliary time series array; Write the valid data records back to the historical backup data and output the updated main time series array and auxiliary time series array.

[0034] In this embodiment, the first step is to acquire the equivalent temperature to be written and the historical backup data of the register. The data reuses the calculation results of the same vehicle-mounted backup DC power supply mentioned earlier. The current cycle limit equivalent temperature is 63.8℃. Taking this vehicle-mounted backup DC power supply sample as an example, the effective threshold range of the equivalent temperature is set to -40℃ to 85℃. The historical backup equivalent temperature of the previous cycle is 63.2℃, and the current communication cycle counter timestamp is 1250. Other vehicle-mounted DC power supply samples of the same model in the batch each hold their own equivalent temperature, effective temperature threshold, historical backup data, and communication timestamp. All data is stored one-to-one according to the device samples to ensure the sample matching of the array write operation. Secondly, the equivalent temperature is used for validity screening, and valid data records with timestamps are generated by combining historical backups: taking the vehicle backup DC power supply sample as an example, the current equivalent temperature of 63.8℃ is within the valid range of -40℃ to 85℃, so 63.8℃ is directly combined with the timestamp 1250 to generate a valid data record with timestamp {temp:63.8℃,timestamp:1250}; if the equivalent temperature of a sample is 90℃, which exceeds the upper limit of the valid temperature, then the historical backup of 63.2℃ is read and the timestamp is used to generate a record with timestamp {temp:63.2℃,timestamp:1250}, so as to avoid invalid temperatures being written into the array.

[0035] Next, the time-stamped valid data records are written to the main timing array, completing the update of the main array data and pointer status. Taking this vehicle-mounted backup DC power supply sample as an example, the fifth-order main timing array is full of 5 storage units. The oldest pointer points to the earliest data position, and the newest pointer points to the position to be written. The old data at the oldest position is cleared, and both pointers move forward synchronously, writing the time-stamped valid data records to the corresponding storage unit of the newest. After the update, the main timing array stores the records as [{temp:61.2℃,timestamp:850},{temp:62.0℃,timestamp:950},{temp:62.7℃,timestamp:1050},{temp:63.2℃,timestamp:1150},{temp:63.8℃,timestamp:1250}]. The main array pointer register and buffer activity flag are written back synchronously. Other power supply samples complete the main timing array writing and updating according to the same pointer logic. Then, based on preset update conditions, the data is selectively written to the secondary timing array. The secondary array update interval is set to 1, and the current count value of the secondary array write counter is 4. The result of taking 4 modulo the update interval is equal to 0. The valid records with time stamps are transferred to the newest position of the third-order secondary timing array via the shadow register. After the update, the secondary timing array is stored as [{temp:62.0℃,timestamp:950},{temp:62.7℃,timestamp:1050},{temp:63.8℃,timestamp:1250}]. If the result of taking the counter modulo is not 0, the data is only stored in the secondary data bypass register, the secondary array pointer is not pushed, and no write operation is performed. Finally, the valid data records with time stamps are written back to overwrite the historical backup register. The historical backup value is updated to 63.8℃, and the updated main timing array and secondary timing array are output. The other power supply samples in the batch perform the same process, outputting the set of timing arrays for each group. The array data is used as input for subsequent calculation of the truncated average temperature. This embodiment filters abnormal temperature data through validity screening and selective writing, ensuring the validity and timing integrity of the data stored in the main and auxiliary time-series arrays.

[0036] In one embodiment, the step of reading the temperature data in the updated main time series array, performing extreme value removal and mean calculation on the temperature data to obtain a truncated average temperature value includes: Read all the temperature data stored in the updated main time series array and output the original temperature data sequence composed of multiple temperature data points; The data sorting processing logic is invoked to perform sorting processing on the original temperature data sequence, and the output elements are rearranged according to their size relationship to form an ordered temperature sequence. Traverse and identify the corresponding extreme value data within the ordered temperature sequence, remove the identified extreme value data, and output the processed intermediate temperature data set. An accumulation operation is performed on all data within the intermediate temperature data group, and then the mean value conversion is performed on the accumulated operation result to obtain the truncated average temperature value.

[0037] In this embodiment, the first step is to acquire all time-stamped temperature data stored within the main time-series array. The data reuses the output results of the same vehicle-mounted backup DC power supply mentioned above. Taking this vehicle-mounted backup DC power supply sample as an example, the updated fifth-order main time-series array stores the data as [{temp:61.2℃,timestamp:850},{temp:62.0℃,timestamp:950},{temp:62.7℃,timestamp:1050},{temp:63.2℃,timestamp:1150},{temp:63.8℃,timestamp:1250}]. Other vehicle-mounted DC power supply samples of the same model within the batch each hold their own updated main time-series arrays. All array data are stored one-to-one according to the device samples to ensure sample matching for subsequent sorting and extreme value removal operations. Next, all temperature data from the main time-series array is read, and the original temperature data sequence is output. Taking the vehicle-mounted backup DC power supply sample as an example, the temperature field of each record in the array is extracted, the timestamp information is removed, and the original temperature data sequence [61.2℃, 62.0℃, 62.7℃, 63.2℃, 63.8℃] is output. If the main time-series array is not yet filled with 5 cells, all valid temperature elements in the array are read to generate the original sequence. The temperature of other power supply samples is extracted in the same way to generate their respective sets of original temperature data sequences. Then, the data sorting processing logic is called to perform ascending sorting on the original temperature data sequence and output an ordered temperature sequence. Taking the vehicle-mounted backup DC power supply sample as an example, the original sequence is rearranged by insertion sort logic to obtain an ordered temperature sequence [61.2℃, 62.0℃, 62.7℃, 63.2℃, 63.8℃]. If there are out-of-order temperature jumps in the original sequence, the sorting logic also performs a rearrangement from smallest to largest. The original sequences of other power supply samples are all sorted to obtain the corresponding sets of ordered temperature sequences.

[0038] Next, the extreme values ​​of the ordered temperature sequence are identified and removed, outputting intermediate temperature data sets. Taking the vehicle-mounted backup DC power supply sample as an example, the ordered sequence is traversed, and the first element (61.2℃) is identified as the minimum extreme value, and the last element (63.8℃) is identified as the maximum extreme value. These two extreme values ​​are removed from the sequence, outputting intermediate temperature data sets [62.0℃, 62.7℃, 63.2℃]. If there are abnormal maxima or minima caused by sensor glitches in the ordered sequence, these are also removed as extreme values. If the array has only 3 valid data points, one maximum and one minimum value are removed. The remaining ordered sequences are processed according to this rule to obtain intermediate temperature data sets for each sample. Finally, the intermediate temperature data sets are summed and averaged to calculate the truncated average temperature value. Taking the vehicle-mounted backup DC power supply sample as an example, the intermediate temperature data set is accumulated: 62.0 + 62.7 + 63.2 = 187.9℃. Then, the mean is calculated: 187.9 ÷ 3 ≈ 62.63℃, yielding a cutoff average temperature of 62.63℃. The remaining power supply samples in the batch are processed using the same procedure to obtain the cutoff average temperature set for each device. This set serves as the input for the recursive least squares operation. This embodiment uses sorting and extreme value removal preprocessing to suppress temperature spike noise interference and improve the stability of the recursive least squares input data.

[0039] In one embodiment, the step of obtaining the temperature change acceleration by subtracting the temperature change rate from the historical temperature change rate, and obtaining the environmental correction coefficient by querying a preset gear table based on the equivalent temperature value and the in-vehicle ambient temperature value, includes: Obtain the temperature change rate and the historical temperature change rate, perform difference calculation on the two and apply amplitude limiting to obtain the temperature change acceleration. The equivalent temperature value and the in-vehicle ambient temperature value are obtained to perform temperature difference calculation. The average ambient temperature difference is obtained by combining the historical ambient temperature difference. Based on the equivalent temperature value and the average ambient temperature difference, the preset gear table is consulted to obtain the candidate gear number; The candidate gear number is compared with the historical gear benchmark, and the basic correction coefficient obtained from the table is weighted to obtain the environmental correction coefficient.

[0040] In this embodiment, the first step is to obtain the temperature change rate and the historical temperature change rate. A difference calculation is then performed on these two rates, followed by amplitude limiting, to obtain the temperature change acceleration. The temperature change rate was obtained in the preceding step S2 by linearly fitting the equivalent temperature value into the sliding time series array. The historical temperature change rate is the baseline change rate stored in the previous sampling period and must be stored one-to-one according to the sampling period to ensure the timing matching of subsequent difference calculations. Taking the vehicle-mounted backup DC power supply as an example, the preceding step has obtained an equivalent temperature value of 63.8℃, corresponding to a temperature change rate k of 0.13℃ / 100ms. The historical temperature change rate k_prev stored in the previous sampling period is 0.08℃ / 100ms. The difference between these two rates yields the original value of the temperature change acceleration: 0.05℃ / 100ms. 2 After compression and dynamic threshold limiting, the acceleration due to temperature change, a, is 0.05℃ / 100ms. 2 The temperature change rate of other sampling periods within the batch is also subtracted from and limited to the historical temperature change rate according to this logic to form a complete temperature change acceleration sequence. Next, the equivalent temperature value and the in-vehicle ambient temperature value are obtained, temperature difference calculation is performed, and a fusion operation is performed with the historical ambient temperature difference to obtain the average ambient temperature difference. The equivalent temperature value comes from the multi-point temperature aggregation and spatial correction in the previous step S1. The in-vehicle ambient temperature value is collected by the in-vehicle ambient temperature sensor: taking the vehicle backup DC power supply as an example, the difference between the equivalent temperature value of 63.8℃ and the in-vehicle ambient temperature value of 45℃ yields an ambient temperature difference of 18.8℃. This is then combined with the ambient temperature differences of 18.2℃, 18.5℃, etc., from various historical sampling periods to perform an average fusion operation, resulting in an average ambient temperature difference of 18.5℃, which reflects the current stability level of the in-vehicle heat dissipation conditions. The ambient temperature differences of other sampling periods within the batch are also fused according to this logic to form a stable average ambient temperature difference sequence.

[0041] Next, the gear position lookup calculation is performed. According to the scheme settings, the preset gear position table is consulted based on the equivalent temperature value and the average ambient temperature difference to obtain the candidate gear position number. The preset gear position table is divided into multiple gear positions according to the intersection of the equivalent temperature range and the average ambient temperature difference range. Considering the characteristics of the vehicle backup DC power supply being prone to heat accumulation under high temperature conditions and requiring fine adjustment, the current equivalent temperature value of 63.8℃ and the average ambient temperature difference of 18.5℃ are used as two-dimensional query coordinates to locate in the preset gear position table, and the candidate gear position number is obtained as the 3rd gear. Next, backlash comparison and weighting are performed. The candidate gear number is compared with the historical gear benchmark. The basic correction coefficient obtained from the lookup table is weighted to obtain the environmental correction coefficient. In this embodiment, the candidate gear number in the current sampling period is the 3rd gear, and the historical gear benchmark is the 2nd gear. The difference between the two does not exceed the preset backlash threshold, so it is determined that the gear has not changed substantially. Therefore, the basic correction coefficient 0.92 corresponding to the 2nd gear and the basic correction coefficient 0.95 corresponding to the 3rd gear are weighted and fused. The weighting is based on the historical gear weight of 0.6 and the current gear weight of 0.4 to obtain the environmental correction coefficient λ of 0.94, which avoids frequent gear jumps at the boundary. If the difference between the candidate gear number and the historical gear benchmark in a certain sampling period exceeds the backlash threshold, the system directly switches to the candidate gear and adopts its corresponding basic correction coefficient to avoid excessive smoothing that leads to response lag. Finally, the environmental correction coefficient is determined. The weighted result is directly used as the environmental correction coefficient for the current sampling period: the environmental correction coefficient λ obtained by the vehicle backup DC power supply under the current operating conditions is 0.94. Other sampling periods within the batch are calculated according to the same process to form a complete environmental correction coefficient sequence, which serves as the input for the power correction calculation in step S4, allowing it to multiply the base power by λ to obtain the corrected power. This embodiment uses differential limiting, temperature difference fusion, and gear hysteresis mechanisms to smoothly obtain the temperature change acceleration and environmental correction coefficient, suppress jitter, and improve the stability and safety of power regulation.

[0042] In one embodiment, the step of obtaining the temperature change rate and the historical temperature change rate, performing a difference calculation on the two and applying a limiting process to obtain the temperature change acceleration includes: Obtain the temperature change rate and historical temperature change rate, perform difference calculation to obtain the original acceleration value, and merge the original acceleration value with the historical acceleration data to obtain a filtered data group; Read the filtered data set, perform a fusion filtering process combining the median and the mean, and obtain the filtered acceleration value; The filtered acceleration value is compressed according to a preset compression boundary to obtain a compression acceleration value; The compression acceleration value is compared with the dynamic limiting threshold and a limiting process is performed to obtain the temperature change acceleration.

[0043] In this embodiment, the first step is to obtain the temperature change rate and the historical temperature change rate, perform a difference operation to obtain the raw acceleration value, and merge the raw acceleration value with the historical acceleration data to obtain a filtered data set. The temperature change rate and the historical temperature change rate are derived from the output of the previous embodiment. The temperature change rate is 0.13℃ / 100ms, and the historical temperature change rate is 0.08℃ / 100ms. These need to be stored one-to-one according to the sampling period time sequence to ensure the matching of time-series operations. Taking the vehicle-mounted backup DC power supply sample as an example, subtracting the historical temperature change rate from the current temperature change rate yields the raw acceleration value of 0.05℃ / 100ms. 2 The original historical acceleration values ​​for the first two cycles, as saved in the register, were 0.04℃ / 100ms. 2 0.06℃ / 100ms 2 The three sets of values ​​are combined to form a three-element filtered data set [0.05, 0.04, 0.06]℃ / 100ms 2 For the remaining sampling periods within the batch, the current and historical temperature change rates are read and interpolated. Historical acceleration values ​​are then concatenated to form corresponding filtered data sets, completing data preparation. Next, the filtered data sets are read, and a fusion filtering process combining the median and mean is performed to obtain the filtered acceleration values: [0.05, 0.04, 0.06]℃ / 100ms for this sample's filtered data set. 2 For example, sorting the array in ascending order yields [0.04, 0.05, 0.06], and extracting the median value is 0.05℃ / 10ms. 2 The arithmetic mean is also 0.05℃ / 100ms. 2 The filtered acceleration value is calculated as follows: weighted fusion with a median weight of 0.6 and a mean weight of 0.4. 2 If the filtered data set contains jump values ​​introduced by sensor noise, for example, if the filtered data set is [0.05, 0.22, 0.06], median filtering can effectively reduce the disturbance caused by abnormal spikes. The filtered data sets of other sampling periods are all processed according to the median-mean weighted logic to obtain the filtered acceleration value corresponding to each period.

[0044] Next, the filtered acceleration value is compressed according to a preset compression boundary to obtain the compression acceleration value. According to the scheme settings, the upper limit threshold of the compression boundary is 0.06℃ / 100ms. 2 The compression lower limit threshold is -0.06℃ / 100ms. 2 The compression ratio is set to 0.5; the filter acceleration for this sample is 0.05℃ / 100ms. 2 Within the upper and lower compression boundary range, a compression acceleration value of 0.05℃ / 100ms is directly assigned. 2When the filtered acceleration exceeds the compression boundary, such as a filtered acceleration of 0.08℃ / 100ms... 2 Then, according to the compression rule: compression upper limit threshold + (0.08-0.06)×0.5, the output compressed value is 0.07℃ / 100ms. 2 This process suppresses excessive acceleration spikes. Then, the compression acceleration value is compared with the dynamic limiting threshold and limiting is performed to obtain the temperature change acceleration. The dynamic limiting threshold is calculated by multiplying the previous week's limiting acceleration by a dynamic coefficient of 0.8. In this embodiment, the previous week's limiting acceleration is 0.06℃ / 100ms. 2 The calculated absolute value of the dynamic limiting threshold is 0.048℃ / 100ms. 2 The compression acceleration of this sample is 0.05℃ / 100ms. 2 Since the absolute value is greater than the dynamic limiting threshold, and considering the positive sign of the compression acceleration, the dynamic limiting threshold is set to 0.048℃ / 100ms. 2 The temperature change acceleration is used as the final limiting output. If the absolute value of the compression acceleration is less than the dynamic limiting threshold, the compression acceleration value is directly output to avoid drastic jumps in acceleration output. Finally, the temperature change acceleration result is output. The final output temperature change acceleration of the vehicle backup DC power supply in this sampling period is 0.048℃ / 100ms. 2 Within each batch, the same process is completed for each sampling period. The output temperature change acceleration sequence is sent to the subsequent environmental correction coefficient calculation step, and also provides input for the safety boundary interpolation calculation in the subsequent step S5. This embodiment uses multi-level filtering, compression, and dynamic limiting to filter out temperature sampling noise, suppress acceleration output jumps, and ensure the stability of on-board power supply regulation.

[0045] In one embodiment, the step of querying a preset gear table based on the equivalent temperature value and the average ambient temperature difference to obtain the candidate gear number includes: Based on the equivalent temperature value, the temperature range positioning table is consulted, and after comparison and coding processing, the original temperature range number is obtained. Perform hysteresis stabilization processing on the original temperature range number and the historical temperature range benchmark to obtain a stable temperature range number. Based on the average ambient temperature difference, a target temperature range table is selected, and a lookup process is performed in conjunction with the stable temperature range number to obtain the original temperature difference range number. Perform hysteresis stabilization processing on the original temperature difference gear number and the historical temperature difference gear benchmark to obtain the candidate gear number.

[0046] In this embodiment, the temperature range positioning table is first queried based on the equivalent temperature value. After comparison and encoding processing, the original temperature level number is obtained. The equivalent temperature value reuses the calculation result from the previous embodiment, which is 63.8℃. The temperature range positioning table is pre-stored in the read-only memory of the vehicle controller. Each temperature level is set with a corresponding boundary threshold. The sampling period data is stored one by one in time sequence to ensure the data matching of the table lookup operation. Taking the vehicle backup DC power supply sample as an example, the equivalent temperature of 63.8℃ is input into the temperature range positioning table and compared with the boundary thresholds of each level in parallel to generate a temperature boundary state vector. After priority encoding processing, the original temperature level number is output as 3. The remaining sampling periods in the batch input their respective equivalent temperatures into the temperature range positioning table in sequence, complete the comparison and encoding, and obtain the set of original temperature level numbers corresponding to each period. Secondly, the original temperature range number and the historical temperature range reference are subjected to hysteresis stabilization processing to obtain a stable temperature range number. The hysteresis threshold is preset to level 1 to suppress temperature oscillation switching at the range boundary. Taking this sample as an example, the historical temperature range reference of the previous cycle stored in the register is read as 2, and the original temperature range number is 3. The difference between the two ranges is equal to the hysteresis threshold of 1, which meets the range switching condition, and the stable temperature range number is output as 3. If the original temperature range number is 3 and the historical temperature range reference is also 3 in a certain sampling cycle, the range remains unchanged. If the difference between the original range and the historical range is less than the hysteresis threshold, the historical temperature range reference remains unchanged to avoid frequent range jumps caused by small temperature disturbances. The remaining sampling cycles in the batch are stabilized according to this hysteresis logic to obtain the stable temperature range numbers of each group.

[0047] Next, the target temperature range table is selected based on the average ambient temperature difference. A lookup process is then performed using the stable temperature range number to obtain the original temperature difference range number. The average ambient temperature difference for this sample is 18.5℃, which is positive. Therefore, the positive temperature difference range table is selected as the target range table. Taking this sample as an example, the stable temperature range number 3 is used as the sub-segment selection index. The corresponding range boundary sub-segment is located in the positive temperature difference range table. The absolute value of the average ambient temperature difference, 18.5℃, is compared in parallel with the boundary thresholds within each sub-segment to generate a boundary vector. This vector is then decoded to obtain the original temperature difference range number 3. If the average ambient temperature difference is negative, the negative temperature difference range table is switched to perform the lookup operation. For other sampling periods within the batch, the corresponding range table is selected based on the sign of its own temperature difference to obtain the original temperature difference range number for each period. Next, the original temperature difference gear number and the historical temperature difference gear benchmark are subjected to hysteresis stabilization processing to obtain candidate gear numbers. The historical temperature difference gear benchmark stored in the previous cycle is read as 2, and the temperature difference gear hysteresis threshold is also set to level 1. If the difference between the original temperature difference gear number 3 and the historical temperature difference gear benchmark 2 in this sample is equal to the hysteresis threshold, a gear update is triggered, and the candidate gear number is output as 3. If there are boundary conditions, and the difference between the original temperature difference gear number and the historical gear number is less than the hysteresis threshold, the historical temperature difference gear benchmark remains unchanged, and the historical gear number is output as the candidate gear number. Finally, the candidate gear number is determined. The on-board backup DC power supply outputs the candidate gear number 3 in this sampling cycle. The same complete process is performed in each sampling cycle within the batch to obtain the candidate gear number sequence of the entire batch, which is output to the subsequent basic environmental correction coefficient reading stage to provide gear index input for the weighted calculation of the environmental correction coefficient. This embodiment adopts two-stage hysteresis stabilization processing to suppress gear boundary oscillations, output stable candidate gear numbers, and ensure the smooth and reliable calculation of the environmental correction coefficient.

[0048] In one embodiment, the step of calculating the predicted temperature using the temperature change rate and equivalent temperature value, interpolating and weighting to obtain the base power, and multiplying it by the environmental correction coefficient to obtain the corrected power value includes: A two-way prediction calculation is performed based on the temperature change rate and the equivalent temperature value, and a conservative prediction temperature is determined by combining the results of the mapping table lookup. Using the conservatively predicted temperature as an index, the power-temperature mapping table is queried, the power data of adjacent temperature ranges is read, and the boundary distance information is calculated. Based on the boundary distance, the transition zone condition is determined, and the power data of adjacent temperature ranges are interpolated or weighted and mixed to obtain the base power value; Obtain the environmental correction factor, and multiply the base power value with the environmental correction factor to obtain the corrected power value.

[0049] In this embodiment, a bidirectional prediction calculation is first performed based on the temperature change rate and the equivalent temperature value. A conservative prediction temperature is determined by combining the results of a mapping table lookup. Both the temperature change rate and the equivalent temperature value reuse the output results from the previous embodiment. The equivalent temperature value is 63.8℃, the temperature change rate is 0.13℃ / 100ms, and the dynamic prediction duration is 300ms obtained from the dynamic duration mapping table. The calculation data for the sampling period are stored sequentially to ensure the timing matching of the prediction calculation. Taking this vehicle-mounted backup DC power supply sample as an example... The forward predicted temperature is obtained by multiplying the temperature change rate by the dynamic prediction duration and then adding the equivalent temperature: 63.8 + 0.13 × 300 = 102.8℃. The reverse predicted temperature is obtained by inverting the temperature change rate and multiplying it by the dynamic prediction duration and then adding the equivalent temperature: 63.8 - 0.13 × 300 = 24.8℃. The smaller of the two values ​​is taken as the conservative predicted temperature of 24.8℃. The remaining sampling periods in the batch are calculated using the same bidirectional prediction logic, and the conservative predicted temperature of each period is output for subsequent mapping table index lookup. Secondly, the power-temperature mapping table is queried using the conservatively predicted temperature as an index. Power data for adjacent temperature ranges is read and boundary distance information is calculated. The power-temperature mapping table is pre-stored in memory using a coarse-to-sub-level two-stage storage structure. Taking this sample as an example, the conservatively predicted temperature of 24.8℃ is input into the coarse-to-temperature mapping table. After comparison and decoding, the coarse range number 1 is obtained. The corresponding target sub-level mapping table is selected. After offset conversion and index truncation processing, the low-level temperature of 20℃ and the high-level temperature of 30℃ for adjacent ranges are read, corresponding to a low-level power of 2200W and a high-level power of 1600W. The lower boundary distance is calculated as 24.8 - 20 = 4.8℃, and the upper boundary distance is calculated as 30 - 24.8 = 5.2℃. The two-stage table lookup and boundary distance calculation are completed in the remaining sampling periods within the batch, generating a complete set of range power and distance information.

[0050] Next, the transition zone condition is determined based on the boundary distance. The power data of adjacent temperature ranges are interpolated or weighted and mixed to obtain the base power value. According to the scheme settings, the transition zone width corresponding to the range in this subdivision mapping table is set to 5℃: the lower boundary distance of this sample is 4.8℃ and the upper boundary distance is 5.2℃. It is determined that the conservatively predicted temperature does not fall into the transition zone interval on both sides of the range, and linear interpolation is directly performed to calculate the base power value P0=2440W. If the conservatively predicted temperature falls into the transition zone range, the interpolation result is weighted and mixed with the power of the range outside the transition zone according to the distance ratio to eliminate the power step jump when the range is switched. The remaining sampling periods in the batch select the interpolation or weighted mixing mode according to the transition zone determination result and output the base power values ​​of each group. Next, the environmental correction coefficient is obtained. The base power value is multiplied by the environmental correction coefficient to obtain the corrected power value. The environmental correction coefficient reuses the output result of the previous embodiment, 0.94. Taking this sample as an example, the base power of 2440W is multiplied by the environmental correction coefficient of 0.94 to obtain the corrected power value of 2293.6W. If the environmental heat dissipation conditions are poor and the environmental correction coefficient decreases, the corrected power is reduced accordingly, realizing the dynamic constraint of heat dissipation conditions on power. Finally, the corrected power value is determined. The on-board backup DC power supply outputs the corrected power value of 2293.6W in this sampling period. The complete set of calculations is completed in each sampling period within the batch to obtain a complete corrected power sequence, which is output to the subsequent step S5 for comparison and selection with the power upper limit. This embodiment suppresses power step jumps and realizes dynamic and smooth adjustment of power according to heat dissipation conditions through bidirectional conservative prediction, transition band weighting, and environmental coefficient correction.

[0051] In one embodiment, the step of querying the power-temperature mapping table using the conservatively predicted temperature as an index, reading the power data of adjacent temperature ranges, and calculating boundary distance information includes: The conservative predicted temperature and the coarse power temperature mapping table are compared and decoded to obtain the coarse division level number. Based on the coarse subdivision number, a target subdivision mapping table is selected, and an offset conversion process is performed on the conservative predicted temperature to obtain the truncated index value. Access the target subdivision mapping table based on the truncated index value, and read the temperature boundary value and power value corresponding to the adjacent gear. The boundary distance information is obtained by performing a difference operation between the conservatively predicted temperature and the temperature boundary value.

[0052] In this embodiment, the conservative predicted temperature and the coarse power temperature mapping table are first compared and decoded to obtain the coarse temperature range number. The conservative predicted temperature reuses the output result of the previous embodiment, 24.8℃. The coarse power temperature mapping table is pre-stored in the vehicle controller ROM. The table stores the temperature boundary threshold of each coarse temperature range. The data of each sampling period are stored one-to-one in time sequence to ensure the data matching of the table lookup. Taking the vehicle backup DC power supply sample as an example, the conservative predicted temperature of 24.8℃ is sent to the parallel comparator array and compared with the boundary thresholds of all ranges in the coarse temperature mapping table at the same time. The coarse temperature boundary is output as a vector. Then, the boundary is decoded through the vector to obtain the coarse temperature range number 1. The remaining sampling periods in the batch send their respective conservative predicted temperatures to the coarse temperature mapping table to complete the comparison and decoding to obtain the coarse temperature range number set corresponding to each period. Secondly, a target subdivision mapping table is selected based on the coarse classification level number. An offset conversion is performed on the conservatively predicted temperature to obtain a truncation index value. Different coarse classification levels are bound to independent subdivision mapping tables to refine the temperature-power mapping relationship and reduce the data storage pressure on a single mapping table. Taking this sample as an example, the coarse classification level number is 1. Based on this, the target subdivision mapping table corresponding to level 1 is selected. The starting temperature of this subdivision mapping table is 20℃, and the preset temperature quantization step size is 5℃. The conservatively predicted temperature is subtracted from the starting temperature of the subdivision table to complete the offset conversion, i.e., 24.8 - 20 = 4.8℃. Then, the offset is divided by the quantization step size to calculate a floating-point index, 4.8 ÷ 5 = 0.96. This is rounded down and truncated at the upper limit to obtain a truncation index value of 0. For the remaining sampling periods within the batch, the corresponding subdivision mapping table is switched according to its own coarse classification level number, and the offset conversion and truncation index calculation are completed sequentially to obtain the truncation index values ​​for each group.

[0053] Next, the target subdivision mapping table is accessed based on the truncated index value to read the temperature boundary values ​​and power values ​​corresponding to adjacent gears. Taking this sample as an example, the truncated index value is 0. The target subdivision mapping table is accessed using this index as the address offset to read the data of the current gear and the next adjacent gear, obtaining the low-gear temperature boundary of 20℃ and the high-gear temperature boundary of 30℃, corresponding to the low-gear power of 2200W and the high-gear power of 1600W. If the truncated index is at the end of the subdivision table, the last two sets of adjacent gear data are read to avoid reading incorrect parameters due to index out-of-bounds reading. The remaining sampling periods in the batch complete the mapping table reading according to the truncated index to obtain the temperature boundary and power data of each set of adjacent gears. Next, the conservatively predicted temperature and the temperature boundary value are compared using a difference operation to obtain boundary distance information. Taking this sample as an example, the conservatively predicted temperature of 24.8℃ minus the low-level temperature boundary of 20℃ yields a lower boundary distance of 4.8℃; the high-level temperature boundary of 30℃ minus the conservatively predicted temperature of 24.8℃ yields an upper boundary distance of 5.2℃. The lower and upper boundary distances together constitute the boundary distance information for this calculation. When the conservative prediction is close to the gear boundary, the distance information can be used for the subsequent transition zone determination logic. The difference operation is completed in all other sampling periods within the batch to generate a complete set of boundary distance information. Finally, the boundary distance information output results are determined. The vehicle backup DC power supply outputs a lower boundary distance of 4.8℃ and an upper boundary distance of 5.2℃ in this sampling period. The same process is performed in each sampling period within the batch. The output gear power and boundary distance information are sent to the subsequent transition zone condition determination stage to provide an index and distance input for basic power interpolation or weighted mixed calculation. This embodiment employs a coarse-to-subtle two-level lookup table mechanism to reduce storage usage and accurately obtain gear parameters and distance information, providing reliable support for subsequent power interpolation calculations.

[0054] In one embodiment, the step of obtaining the maximum permissible current based on the temperature change acceleration query safety boundary, calculating the power upper limit, and selecting the smaller value as the target power includes: The acceleration values ​​of temperature change are obtained and written into a sliding array. Extreme value removal and mean operation are performed on the acceleration data in the array to obtain the truncated and filtered acceleration values. Based on the truncated filter acceleration value, the safety boundary curve table is consulted, and interpolation is performed to obtain the spline interpolated current value; A two-dimensional query coordinate system is constructed using the truncated filter acceleration value and the equivalent temperature value. The safety boundary surface is queried and interpolation is performed to obtain the bilinear interpolated current value. The maximum allowable charge / discharge current value is obtained by performing a fusion operation on the spline interpolation current value and the bilinear interpolation current value. The maximum allowable charge / discharge current value is then multiplied by the battery terminal voltage to obtain the upper limit of power. The upper limit power value and the corrected power value are compared numerically to select the smaller value to obtain the safe power. The rate of change constraint processing is applied to the safe power to obtain the target power.

[0055] In this embodiment, the temperature change acceleration value is first acquired and written into a sliding array. Extreme value removal and mean averaging are then performed on the acceleration data in the array to obtain a truncated filtered acceleration value. The temperature change acceleration value reuses the output result from the previous embodiment, which is 0.048℃ / 100ms. 2 The fifth-order acceleration sliding array is used to store the acceleration data of the most recent five sampling periods. The data of each sampling period are stored in a one-to-one correspondence according to the time sequence to ensure the matching of timing operations: taking the vehicle backup DC power supply sample as an example, the current temperature change acceleration is 0.048℃ / 100ms. 2 Write the fifth-order acceleration sliding array, remove the oldest historical data from the array, and the updated acceleration data in the array is [0.042, 0.045, 0.046, 0.050, 0.048]℃ / 100ms. 2 Sort the data within the array in ascending order and remove the maximum value of 0.050℃ / 100ms. 2 With a minimum value of 0.042℃ / 100ms 2 The remaining three sets of acceleration data [0.045, 0.046, 0.048] ℃ / 100ms 2 The arithmetic mean was calculated, yielding a cutoff filter acceleration value of 0.0463℃ / 10ms. 2 If abnormal peak accelerations caused by sensor disturbances exist within the sliding array, the extreme value removal step can filter out abnormal extreme values. For the remaining sampling periods within the batch, the sliding update and extreme value removal mean calculation are performed according to this logic to obtain the truncated filtered acceleration values ​​for each group. Next, the safety boundary curve table is queried based on the truncated filtered acceleration values, and interpolation is performed to obtain the spline interpolated current values. The safety boundary curve table is pre-stored in the controller's read-only memory, storing multiple sets of acceleration nodes and corresponding allowable current control points. Taking this sample as an example, the truncated filtered acceleration is 0.0463℃ / 100ms. 2 The system performs parallel comparison and decoding with all acceleration nodes in the safety boundary curve table. It then performs hysteresis stabilization processing by combining the historical node index to obtain the stable lower node index and boundary condition flag. It reads three adjacent sets of control points to construct a quadratic spline polynomial, substitutes the truncated filtered acceleration into the polynomial operation, and then performs weighted averaging, current change rate and current upper limit constraint by combining the spline current of the previous period. Finally, it obtains the spline interpolated current value of 94A. In the remaining sampling periods within the batch, the system sequentially completes node decoding, hysteresis stabilization, spline interpolation and constraint processing, and outputs the spline interpolated current values ​​of each set.

[0056] Next, a two-dimensional query coordinate system is constructed using the truncated filter acceleration value and the equivalent temperature value. The safety boundary surface is queried and interpolation is performed to obtain the bilinear interpolated current value. The equivalent temperature is reused from the previous value of 63.8℃, and the truncated filter acceleration is 0.0463℃ / 100ms. 2 Using the equivalent temperature of 63.8℃ as the two-dimensional query coordinates, the two-dimensional grid formed by adjacent acceleration nodes and adjacent temperature nodes is located on the safety boundary surface. The allowable current values ​​stored at the four corners of the grid are read, and bilinear interpolation is performed along the temperature axis and acceleration axis respectively to calculate the bilinear interpolated current value of 91A. For the remaining sampling periods in the batch, the two-dimensional surface interpolation is completed using their respective truncated filtered acceleration and equivalent temperature to obtain the corresponding bilinear interpolated current. Then, the spline interpolated current value and the bilinear interpolated current value are fused to obtain the maximum allowable charge and discharge current value. The maximum allowable charge and discharge current value is multiplied by the battery terminal voltage to obtain the upper limit of power. The first weighting coefficient is preset to 0.6 and the second weighting coefficient is preset to 0.4. The battery terminal voltage reuse case parameter is 27.2V. Taking this sample as an example, the maximum allowable charge and discharge current value = 94×0.6+91×0.4=92.8A; the upper limit of power value = 92.8×27.2=2524.16W. The other sampling periods in the batch are fused with the same weight, and the upper limit of power value for each period is calculated by combining the real-time battery terminal voltage.

[0057] Finally, the upper limit power value and the corrected power value are compared numerically to select the smaller value to obtain the safe power. The change rate constraint processing is performed on the safe power to obtain the target power. The corrected power reuses the previous result of 2293.6W, the historical power upper limit of the previous period is 2100W, the power rise change threshold is 120W, and the minimum allowable power is 800W. Taking this sample as an example, the smaller value of the upper limit power of 2524.16W and the corrected power of 2293.6W is taken to obtain the safe power of 2293.6W. The power change is calculated as 2293.6 - 2100 = 193.6W, which exceeds the rise change threshold of 120W. The power change is constrained to 120W. The historical power upper limit is added to obtain 2220W. The larger value is taken from the minimum allowable power to output the target power of 2220W. The comparison and power change rate limiting processing are completed in the remaining sampling periods within the batch, and the complete target power sequence is output and sent to the power converter to complete the entire charging and discharging control closed loop. This embodiment combines one-dimensional and two-dimensional interpolation to obtain the maximum allowable current, and dual-constrained power output effectively suppresses heat accumulation and improves the operational safety of the vehicle power supply.

[0058] In one embodiment, the step of querying the safety boundary curve table based on the truncated filter acceleration value and performing interpolation to obtain the spline interpolated current value includes: The truncated filtered acceleration value is compared and decoded with the node values ​​in the safety boundary curve table to obtain the original lower node index, and the historical index reference is read. Perform backlash stabilization processing on the original lower node index and the historical index benchmark to obtain a stable lower node index and obtain the boundary condition flag. Based on the stable lower node index, read the node data in the safety boundary curve table, select the interpolation mode and construct a quadratic spline polynomial by combining the boundary condition flags; Substituting the truncated filtered acceleration value into the quadratic spline polynomial, and combining it with historical current data to perform fusion and constraint processing, the spline interpolated current value is obtained.

[0059] In this embodiment, the truncated filtered acceleration value is first compared and decoded with the node values ​​in the safety boundary curve table to obtain the original lower node index. Then, the historical index reference is read, and the truncated filtered acceleration value reuses the output result from the previous embodiment: 0.0463℃ / 100ms. 2 The safety boundary curve table is pre-stored in the vehicle controller ROM, storing multiple sets of acceleration nodes and corresponding current control points. Data from each sampling period is stored sequentially to ensure data matching for table lookup operations. Taking this vehicle backup DC power supply sample as an example, the truncated and filtered acceleration is 0.0463℃ / 100ms. 2 Parallel comparison is performed with all acceleration nodes in the safety boundary curve table. The generated node is obtained by decoding logic through vectors, and the original lower node index is 2. At the same time, the historical index benchmark of the previous cycle stored in the register is read as 1. In the remaining sampling cycles within the batch, the truncated filtered acceleration is compared and decoded with the nodes in the table one by one to obtain the original lower node index corresponding to each cycle, and the corresponding historical index benchmark is read synchronously. Secondly, hysteresis stabilization processing is performed on the original lower-end node index and the historical index benchmark to obtain a stable lower-end node index and obtain the boundary condition flag. The node index hysteresis threshold is set to 1 to suppress frequent index jumps caused by small acceleration disturbances. Taking this sample as an example, the original lower-end node index is 2, the historical index benchmark is 1, the index difference is equal to the hysteresis threshold, the switching condition is met, the stable lower-end node index is output as 2, and the boundary condition flag pre-stored in this index interval is read as the standard mode. If the difference between the original index and the historical index in a certain sampling period is less than the hysteresis threshold, the historical index benchmark is kept unchanged. If the absolute value of the truncated filtered acceleration is greater than the interpolation mode threshold, the high acceleration default node index is directly overwritten. Hysteresis stabilization judgment is performed in the remaining sampling periods in the batch to obtain the stable lower-end node index and boundary condition flag for each group.

[0060] Next, the node data in the safety boundary curve table is read based on the stable lower node index. The interpolation mode is selected and a quadratic spline polynomial is constructed in conjunction with the boundary condition flags. Taking this sample as an example, the stable lower node index is 2. The acceleration and current control points of the previous, lower, and next nodes corresponding to the index are read. The boundary condition flag is in standard mode. The lower and next nodes are selected to complete linear interpolation to obtain an intermediate estimate, which is then combined with the previous node to construct a quadratic spline polynomial. If the boundary condition flag is in extrapolation mode, the three original data points are directly used to construct the quadratic spline polynomial. For other sampling periods within the batch, the interpolation mode is switched according to their respective boundary condition flags to complete the polynomial construction. Then, the truncated filtered acceleration value is substituted into the quadratic spline polynomial, and fusion and constraint processing are performed in conjunction with historical current data to obtain the spline interpolated current value. Taking this sample as an example, the truncated filtered acceleration is 0.0463℃ / 100ms. 2 Substituting the constructed quadratic spline polynomial, the polynomial output estimate of 94.8A is obtained. This is then weighted and averaged with the previous period's spline current value of 93A using weighting coefficients of 0.7 and 0.3, resulting in a weighted average of 94.26A. This is then processed through current change rate limit and current safety limit constraints, ultimately yielding a spline interpolated current value of 94A. If the calculated result exceeds the hardware's allowable current limit, it is directly clamped to the current safety limit to prevent the output of illegal current values. Finally, the spline interpolated current output is determined. The on-board backup DC power supply outputs a spline interpolated current value of 94A in this sampling period. The same process is performed in each sampling period within the batch, outputting a complete spline interpolated current sequence, which is then used in subsequent current weighted fusion steps to calculate the maximum allowable charging and discharging current together with the bilinear interpolated current. This embodiment suppresses current calculation jitter and outputs a stable spline interpolated current by using index hysteresis, mode selection, and quadratic spline interpolation, thus strengthening power supply current safety constraints.

[0061] refer to Figure 2 This application also provides a vehicle-mounted DC power supply charging and discharging control system based on temperature monitoring, including: The equivalent temperature calculation module 100 is used to obtain the temperature values ​​of multiple points inside the vehicle DC power supply and the temperature values ​​of the vehicle interior environment, aggregate the multiple temperature values ​​and combine them with spatial correction processing to obtain the equivalent temperature value. Temperature change rate calculation module 200 is used to store the equivalent temperature value into a time series array and perform linear fitting to obtain the temperature change rate; The environmental correction coefficient acquisition module 300 is used to obtain the temperature change acceleration by subtracting the temperature change rate from the historical temperature change rate, and to obtain the environmental correction coefficient by querying a preset gear table based on the equivalent temperature value and the in-vehicle ambient temperature value. The corrected power calculation module 400 is used to calculate the predicted temperature using the temperature change rate and equivalent temperature value, interpolate and weight the base power, and multiply it by the environmental correction coefficient to obtain the corrected power value. The target power determination module 500 is used to obtain the maximum allowable current by querying the safety boundary based on the temperature change acceleration, calculate the upper limit of power, and select the smaller value as the target power.

[0062] Reference Figure 3 This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 3 As shown, this computer device includes a processor, memory, network interface, and database connected via a bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores operations, computer programs, and the database. The internal memory provides an environment for the operation and execution of the computer programs stored in the non-volatile storage media. The database stores data such as a temperature-monitored vehicle DC power supply charging and discharging control method. The network interface is used for communication with external terminals via a network connection. When executed by a processor, this computer program implements a vehicle-mounted DC power supply charging and discharging control method based on temperature monitoring, including the following steps: acquiring multiple temperature values ​​inside the vehicle-mounted DC power supply and the ambient temperature value inside the vehicle; aggregating the multiple temperature values ​​and combining them with spatial correction processing to obtain an equivalent temperature value; storing the equivalent temperature value in a time series array and performing linear fitting to obtain a temperature change rate; subtracting the temperature change rate from the historical temperature change rate to obtain the temperature change acceleration; querying a preset gear table based on the equivalent temperature value and the ambient temperature value inside the vehicle to obtain an environmental correction coefficient; calculating the predicted temperature using the temperature change rate and the equivalent temperature value, interpolating and weighting to obtain the base power, multiplying it by the environmental correction coefficient to obtain the corrected power value; querying the safety boundary based on the temperature change acceleration to obtain the maximum allowable current, calculating the power upper limit, and selecting the smaller value as the target power.

[0063] One embodiment of this application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements a vehicle DC power supply charging and discharging control method based on temperature monitoring, including the following steps: acquiring multiple temperature values ​​inside the vehicle DC power supply and the ambient temperature value inside the vehicle; aggregating the multiple temperature values ​​and combining them with spatial correction processing to obtain an equivalent temperature value; storing the equivalent temperature value in a time series array and performing linear fitting to obtain a temperature change rate; subtracting the temperature change rate from the historical temperature change rate to obtain a temperature change acceleration; querying a preset gear table based on the equivalent temperature value and the ambient temperature value inside the vehicle to obtain an environmental correction coefficient; calculating a predicted temperature using the temperature change rate and the equivalent temperature value; interpolating and weighting the predicted temperature to obtain a base power; multiplying the base power by the environmental correction coefficient to obtain a corrected power value; querying a safety boundary based on the temperature change acceleration to obtain the maximum allowable current; calculating the upper limit of power; and selecting the smaller value as the target power.

[0064] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media provided in this application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0065] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for controlling the charging and discharging of an on-board DC power supply based on temperature monitoring, characterized in that, include: The system acquires multiple temperature values ​​inside the vehicle's DC power supply and the ambient temperature inside the vehicle. It then aggregates these multiple temperature values ​​and combines them with spatial correction processing to obtain an equivalent temperature value. The equivalent temperature value is stored in a time series array and linear fitting is performed to obtain the temperature change rate. The temperature change acceleration is obtained by subtracting the temperature change rate from the historical temperature change rate. The environmental correction coefficient is obtained by querying the preset gear table based on the equivalent temperature value and the in-vehicle ambient temperature value. The predicted temperature is calculated using the temperature change rate and equivalent temperature value, interpolated and weighted to obtain the base power, and multiplied by the environmental correction coefficient to obtain the corrected power value. The maximum allowable current is obtained by querying the safety boundary based on the acceleration due to temperature change. The upper limit of power is then calculated, and the smaller value is selected as the target power.

2. The on-board DC power supply charging and discharging control method based on temperature monitoring according to claim 1, characterized in that, The step of obtaining multiple temperature values ​​inside the vehicle's DC power supply and the ambient temperature inside the vehicle, aggregating the multiple temperature values ​​and combining them with spatial correction processing to obtain an equivalent temperature value includes: Acquire multiple temperature values ​​inside the vehicle's DC power supply and the ambient temperature inside the vehicle; The internal multi-point temperature values ​​are divided into multiple regional temperature subarrays. Outlier removal processing is performed on each regional temperature subarray to obtain a regional robust array. The representative temperature of each region is obtained by performing aggregation operation on the robust array of each region, and the heat source temperature and the average temperature of the non-heat source region are determined based on the difference of the representative temperature of each region. Based on the dispersion of internal multi-point temperature values, a spatial correction coefficient is queried. The difference between the heat source temperature and the average temperature of the non-heat source area is combined with the spatial correction coefficient to obtain an adaptive correction increment. The adaptive correction increment is superimposed on the heat source temperature, and then temperature limiting processing is performed to obtain the equivalent temperature value.

3. The on-board DC power supply charging and discharging control method based on temperature monitoring according to claim 1, characterized in that, The step of storing the equivalent temperature value into a time series array and performing linear fitting to obtain the temperature change rate includes: Write the equivalent temperature value into the main time series array and the auxiliary time series array to obtain the updated main time series array and auxiliary time series array; Read the temperature data in the updated main time series array, perform extreme value removal and mean calculation on the temperature data, and obtain the truncated average temperature value. Obtain the covariance data and gain vector of the previous period, and perform recursive least squares operation based on the truncated average temperature value to obtain the estimated temperature change rate. The temperature data in the updated auxiliary time series array is read and fitted to obtain the slope value of the auxiliary window. The estimated temperature change rate is then fused with the slope value of the auxiliary window to obtain the temperature change rate.

4. The on-board DC power supply charging and discharging control method based on temperature monitoring according to claim 1, characterized in that, The step of obtaining the temperature change acceleration by subtracting the temperature change rate from the historical temperature change rate, and then querying a preset gear table based on the equivalent temperature value and the in-vehicle ambient temperature value to obtain the environmental correction coefficient includes: Obtain the temperature change rate and the historical temperature change rate, perform difference calculation on the two and apply amplitude limiting to obtain the temperature change acceleration. The equivalent temperature value and the in-vehicle ambient temperature value are obtained to perform temperature difference calculation. The average ambient temperature difference is obtained by combining the historical ambient temperature difference. Based on the equivalent temperature value and the average ambient temperature difference, the preset gear table is consulted to obtain the candidate gear number; The candidate gear number is compared with the historical gear benchmark, and the basic correction coefficient obtained from the table is weighted to obtain the environmental correction coefficient.

5. The on-board DC power supply charging and discharging control method based on temperature monitoring according to claim 4, characterized in that, The step of obtaining the temperature change rate and the historical temperature change rate, performing difference calculation on the two and limiting the amplitude to obtain the temperature change acceleration includes: Obtain the temperature change rate and historical temperature change rate, perform difference calculation to obtain the original acceleration value, and merge the original acceleration value with the historical acceleration data to obtain a filtered data group; Read the filtered data set, perform a fusion filtering process combining the median and the mean, and obtain the filtered acceleration value; The filtered acceleration value is compressed according to a preset compression boundary to obtain a compression acceleration value; The compression acceleration value is compared with the dynamic limiting threshold and a limiting process is performed to obtain the temperature change acceleration.

6. The on-board DC power supply charging and discharging control method based on temperature monitoring according to claim 1, characterized in that, The steps of calculating the predicted temperature using the temperature change rate and equivalent temperature value, interpolating and weighting to obtain the base power, and multiplying it by the environmental correction coefficient to obtain the corrected power value include: A two-way prediction calculation is performed based on the temperature change rate and the equivalent temperature value, and a conservative prediction temperature is determined by combining the results of the mapping table lookup. Using the conservatively predicted temperature as an index, the power-temperature mapping table is queried, the power data of adjacent temperature ranges is read, and the boundary distance information is calculated. Based on the boundary distance, the transition zone condition is determined, and the power data of adjacent temperature ranges are interpolated or weighted and mixed to obtain the base power value; Obtain the environmental correction factor, and multiply the base power value with the environmental correction factor to obtain the corrected power value.

7. The on-board DC power supply charging and discharging control method based on temperature monitoring according to claim 6, characterized in that, The step of querying the power-temperature mapping table using the conservatively predicted temperature as an index, reading the power data of adjacent temperature ranges, and calculating the boundary distance information includes: The conservative predicted temperature and the coarse power temperature mapping table are compared and decoded to obtain the coarse division level number. Based on the coarse subdivision number, a target subdivision mapping table is selected, and an offset conversion process is performed on the conservative predicted temperature to obtain the truncated index value. Access the target subdivision mapping table based on the truncated index value, and read the temperature boundary value and power value corresponding to the adjacent gear. The boundary distance information is obtained by performing a difference operation between the conservatively predicted temperature and the temperature boundary value.

8. The on-board DC power supply charging and discharging control method based on temperature monitoring according to claim 1, characterized in that, The steps of obtaining the maximum permissible current based on the temperature change acceleration query safety boundary, calculating the power upper limit, and selecting the smaller value as the target power include: The acceleration values ​​of temperature change are obtained and written into a sliding array. Extreme value removal and mean operation are performed on the acceleration data in the array to obtain the truncated and filtered acceleration values. Based on the truncated filter acceleration value, the safety boundary curve table is consulted, and interpolation is performed to obtain the spline interpolated current value; A two-dimensional query coordinate system is constructed using the truncated filter acceleration value and the equivalent temperature value. The safety boundary surface is queried and interpolation is performed to obtain the bilinear interpolated current value. The maximum allowable charge / discharge current value is obtained by performing a fusion operation on the spline interpolation current value and the bilinear interpolation current value. The maximum allowable charge / discharge current value is then multiplied by the battery terminal voltage to obtain the upper limit of power. The upper limit power value and the corrected power value are compared numerically to select the smaller value to obtain the safe power. The rate of change constraint processing is applied to the safe power to obtain the target power.

9. The on-board DC power supply charging and discharging control method based on temperature monitoring according to claim 8, characterized in that, The step of querying the safety boundary curve table based on the truncated filter acceleration value, performing interpolation calculations, and obtaining the spline interpolated current value includes: The truncated filtered acceleration value is compared and decoded with the node values ​​in the safety boundary curve table to obtain the original lower node index, and the historical index reference is read. Perform backlash stabilization processing on the original lower node index and the historical index benchmark to obtain a stable lower node index and obtain the boundary condition flag. Based on the stable lower node index, read the node data in the safety boundary curve table, select the interpolation mode and construct a quadratic spline polynomial by combining the boundary condition flags; Substituting the truncated filtered acceleration value into the quadratic spline polynomial, and combining it with historical current data to perform fusion and constraint processing, the spline interpolated current value is obtained.

10. A vehicle-mounted DC power supply charging and discharging control system based on temperature monitoring, characterized in that, include: The equivalent temperature calculation module is used to obtain the temperature values ​​of multiple points inside the vehicle DC power supply and the temperature value of the vehicle interior environment. It aggregates the temperature values ​​of multiple points and combines them with spatial correction processing to obtain the equivalent temperature value. The temperature change rate calculation module is used to store the equivalent temperature value into a time series array and perform linear fitting to obtain the temperature change rate; The environmental correction coefficient acquisition module is used to obtain the temperature change acceleration by subtracting the temperature change rate from the historical temperature change rate, and to obtain the environmental correction coefficient by querying a preset gear table based on the equivalent temperature value and the in-vehicle ambient temperature value. The corrected power calculation module is used to calculate the predicted temperature using the temperature change rate and equivalent temperature value, interpolate and weight the base power, and multiply it by the environmental correction coefficient to obtain the corrected power value. The target power determination module is used to query the safety boundary based on the temperature change acceleration to obtain the maximum allowable current, calculate the upper limit of power, and select the smaller value as the target power.

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