Intelligent partition heating method and system suitable for new energy automobile battery

Abstract

The invention discloses an intelligent partition heating method and system suitable for a new energy automobile battery, and the method comprises the steps: calculating a heating efficiency coefficient of each partition; taking the geometric center position of the area with the maximum heating efficiency coefficient as a temperature inertia center; dividing the battery pack into an inner layer area and an outer layer area by taking a temperature inertia center; calculating a first temperature difference of the inner layer area and a second temperature difference of the outer layer area; taking the ratio of the first temperature difference to the second temperature difference as a heat dissipation calibration parameter; calculating a temperature compensation coefficient of each partition; the adjacent partitions with the temperature compensation coefficient difference smaller than a preset threshold value are combined into the same heating unit; determining heating power according to the temperature compensation coefficient of the heating unit; and the heaters of the heating units are sequentially started according to the sequence of the temperature compensation coefficients from large to small, and heating control is conducted on the battery pack with the heating power. The heating control method and device are used for improving the heating control accuracy and efficiency of the new energy automobile battery.

Description

Technical Field

[0001] This application belongs to the field of battery zone heating, and in particular relates to an intelligent zone heating method and system suitable for new energy vehicle batteries. Background Technology

[0002] In low-temperature environments, the power batteries of new energy vehicles experience problems such as increased electrolyte viscosity and reduced ion migration rate, leading to decreased battery capacity, low charging efficiency, and impacting the vehicle's range and performance. To maintain the battery within a suitable operating temperature range, a battery heating system is typically used to heat the entire battery pack. However, this global heating method suffers from drawbacks such as excessive energy consumption and uneven heating, especially when the internal temperature distribution of the battery pack is uneven, where some areas may be overheated while others remain too cold.

[0003] In related technologies, a thermodynamic model of the battery pack is established, combined with historical temperature data and current environmental parameters, to predict the temperature change trends of various regions over a future period. Based on this prediction, the operating status of the heaters in each zone is adjusted in advance. This method divides the battery pack into multiple independent heating zones, each equipped with a temperature sensor and an adjustable-power heating element. The system calculates the future temperature requirements of each zone using a predictive algorithm, and proactively starts or adjusts the heating power of the corresponding zone. This achieves active response and precise control to temperature changes, improving heating efficiency and temperature uniformity.

[0004] However, during long-distance high-speed driving, the battery pack accumulates a large amount of heat due to continuous discharge. While the temperature is relatively high and evenly distributed across different areas, when the vehicle suddenly stops in a low-temperature environment, different areas of the battery pack experience varying rates of temperature drop due to differences in heat dissipation conditions. Areas closer to the outer edge of the vehicle cool down faster than those closer to the inner edge. The aforementioned technologies struggle to quantify and compensate for these differences in cooling rates caused by varying heat dissipation conditions in different sections of the battery pack after the vehicle has stopped in a hot state, thus reducing the accuracy and efficiency of heating control. Summary of the Invention

[0005] This application provides an intelligent zoned heating method and system for new energy vehicle batteries, which improves the accuracy and efficiency of heating control for new energy vehicle batteries.

[0006] In the first aspect, this application provides an intelligent zone heating method for new energy vehicle batteries, which calculates the heating efficiency coefficient of each zone based on the real-time temperature data of each zone of the battery pack and the corresponding discharge data when the vehicle is in motion. Based on the distribution of the heating efficiency coefficients of each zone, the geometric center of the region with the largest heating efficiency coefficient is taken as the temperature inertia center. Using the temperature inertia center as a reference point, the battery pack is divided into an inner layer region and an outer layer region; Detect the ambient temperature when the vehicle is parked, and calculate the first temperature difference between the average temperature of the inner layer area and the ambient temperature, as well as the second temperature difference between the average temperature of the outer layer area and the ambient temperature. The ratio of the first temperature difference to the second temperature difference is used as the heat dissipation calibration parameter; Multiply the heat dissipation efficiency coefficient of each zone by the heat dissipation calibration parameters to obtain the temperature compensation coefficient of each zone. Adjacent zones with a temperature compensation coefficient difference less than a preset threshold are merged into the same heating unit; If the current temperature of any heating unit is detected to be lower than the preset operating temperature, the heating power is determined according to the temperature compensation coefficient of the heating unit. The heaters of each heating unit are activated sequentially in descending order of temperature compensation coefficient, and the battery pack is heated and controlled by the heating power.

[0007] By adopting the above technical solution, a heating efficiency coefficient is constructed by combining the temperature of each zone during driving with the corresponding discharge amount. This coefficient reflects the unit temperature rise capability under the same electrical power input and includes the thermal response differences caused by local structural and material variations. Based on this, the geometric center of the region with the largest heating efficiency coefficient is selected as the temperature inertia center, and the inner and outer layer regions are divided accordingly. This ensures that the geometric division is consistent with the thermal inertia distribution, thereby explicitly incorporating the differences in different heat dissipation paths into subsequent parameter calibration. The ratio of the two sets of temperature differences between the ambient temperature and the average temperature of the inner and outer layers measured during the parking phase is used as a heat dissipation calibration parameter, allowing for the transformation of heat dissipation intensity caused by different locations in each zone. As a measurable correction factor, the temperature compensation coefficient, obtained by multiplying the heating efficiency, simultaneously characterizes both self-heating capacity and heat dissipation loss, thus more directly representing the actual heat input intensity required for a zone to reach its target temperature. Adjacent zones with small differences in compensation coefficients are merged into a unified heating unit, reducing thermal coupling oscillations at the control unit boundary and frequent device start-stop cycles, improving execution stability and energy utilization continuity. When any unit temperature is below the operating threshold, the power is determined based on the compensation coefficient, and units are started sequentially from largest to smallest coefficient, prioritizing limited heating resources for areas with higher heat demand and stronger heat dissipation, while avoiding overheating of areas with low thermal inertia or weak heat dissipation. This improves the accuracy and efficiency of heating control for new energy vehicle batteries.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the heat dissipation efficiency coefficient of each zone of the battery pack is calculated based on real-time temperature data and corresponding discharge data of each zone during vehicle operation, specifically including: Real-time temperature data of each zone of the battery pack and ambient temperature data are collected during a preset time period when the vehicle is in motion, and the temperature gradient of each zone relative to the ambient temperature is calculated. The preset time period is evenly divided into several time windows. Battery discharge current and voltage data are collected in each time window, and the average discharge power in the time window is calculated. The rate of change of the temperature gradient is calculated for each time window to obtain the instantaneous temperature rise rate of each zone; The instantaneous temperature rise rate is correlated with the average discharge power of the corresponding time window to calculate the temperature rise contribution value per unit discharge power. Calculate the heat transfer loss between adjacent zones to correct the temperature rise contribution value; The corrected temperature rise contribution value is weighted with the actual capacity of the battery pack to obtain the capacity-standardized heat generation characteristic value. The deviation ratio between the heating characteristic value of each zone and the preset standard heating characteristic value is used as the heating efficiency coefficient.

[0009] By adopting the above technical solution, the temperature gradient relative to the ambient temperature is used as the temperature characterization quantity, which weakens the direct impact of environmental fluctuations on the measurement. Furthermore, by dividing the driving time period into equal time windows and calculating the average discharge power and temperature gradient change rate within each window, the input power and temperature rise response are correlated on the same time scale, making the unit power temperature rise contribution value closer to the causal relationship. Considering the heat transfer loss between adjacent zones, the temperature rise contribution value is corrected, eliminating the artificial temperature rise caused by lateral heat diffusion, thereby reducing the interference of structural layout and heat dissipation path coupling on the results. This improves the physical orientation and robustness of the coefficients, reduces systematic deviations caused by environment, structure, and capacity, and makes the determination of heating efficiency more stable and reliable. Consequently, under the same control strategy and power constraints, it reduces temperature control deviation, shortens temperature convergence time, and suppresses unnecessary energy consumption.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the heating power is determined based on the temperature compensation coefficient of the heating unit, specifically including: Obtain the temperature difference between the current temperature and the preset operating temperature of each heating unit; Based on the temperature compensation coefficient of each heating unit, the equivalent temperature difference after temperature compensation is calculated. Calculate the maximum heating power that can be allocated per unit volume based on the total power limit of the battery pack and the volume of each heating unit; The equivalent temperature difference is converted into a standardized temperature difference coefficient, which is the ratio of the equivalent temperature difference of each heating unit to the maximum equivalent temperature difference. The maximum heating power that can be allocated per unit volume is distributed based on the standardized temperature difference coefficient, thus obtaining the heating power of each heating unit.

[0011] By adopting the above technical solution, the temperature difference between the relative operating temperatures of each heating unit is first used as a demand characterization, and then the equivalent temperature difference is obtained by combining the temperature compensation coefficient, avoiding underestimation of the required power for units with strong heat dissipation capacity or weak self-heating. Under the constraints of total power and the volume of each unit, the maximum heating power that can be allocated per unit volume is calculated, and the electrical and thermal safety boundaries are brought forward into the allocation rules to avoid material stress and temperature overshoot caused by individual units obtaining excessively high heat flux density due to small volume. The equivalent temperature difference is normalized to the maximum equivalent temperature difference to form a standardized temperature difference coefficient, and power is allocated in a continuous weighting manner to reduce the oscillation risk of the temperature control loop. The resulting power allocation result satisfies the total power constraint, makes the power flow proportional to the heat demand intensity, and matches the unit's bearing capacity, resulting in a more targeted and controllable temperature rise process, improved overall convergence speed and reduced overshoot probability, reduced temperature difference in the battery pack space, and improved energy utilization efficiency.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before calculating the heat efficiency coefficient of each zone, the method further includes: detecting the number of accelerations and decelerations of the vehicle, and when the number of accelerations and decelerations exceeds a preset frequency, determining that the vehicle is in complex urban conditions; Under complex urban conditions, obtain the temperature decay curves of each zone during two adjacent parking processes; Based on the temperature decay curve, the cumulative heat value of each zone is calculated using the heat diffusion equation; The deviation between the accumulated heat value and the battery temperature of each zone is used as the heat migration correction factor; The real-time temperature data of each zone is weighted and calculated with the heat migration correction factor to obtain the temperature data after heat migration correction. Temperature data corrected for heat migration is used to replace real-time temperature data for each zone in order to calculate the heating efficiency coefficient for each zone.

[0013] By adopting the above technical solution, under complex urban conditions, the temperature decay curves of two adjacent parking processes are obtained, and the heat accumulation value of each zone is calculated based on the heat diffusion equation. This allows residual heat and lateral heat migration to be quantified into calculable thermal state quantities, rather than simply relying on instantaneous temperature. The deviation between the heat accumulation value and the battery temperature of each zone is used to form a heat migration correction coefficient, which is then used to weight and correct the real-time temperature. This makes the observed temperature closer to the effective temperature that reflects the zone's own heat generation capacity, reducing misjudgments caused by residual heat penetration from adjacent areas. The corrected temperature data is then used in the heat generation efficiency estimation process, which can maintain the consistency and comparability of the estimation under frequent start-stop and non-uniform heat dissipation conditions, reducing the impact of operating condition switching on coefficient stability. In urban congestion and low-speed stop-and-go environments, heating triggering is more accurate, ineffective heating is reduced, and local overheating and temperature lag are suppressed, thus obtaining a smoother temperature trajectory and a smaller spatial temperature difference under energy-constrained conditions.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the cumulative heat value of each zone is calculated using a heat diffusion equation based on the temperature decay curve, specifically including: The temperature decay curve is divided into multiple sampling points with equal time intervals in the time dimension; At each sampling point, the temperature gradient between each zone and its adjacent zones is obtained; Based on the thermal conductivity and geometric dimensions of each zone, the boundary conditions of the heat diffusion equation are established. Substitute the temperature gradient of each partition at each sampling point into the heat diffusion equation to calculate the instantaneous heat flux density between adjacent partitions; The instantaneous heat flux density is integrated over time to obtain the total heat transfer of each zone during the parking process; The initial heat before parking in each zone is added together with the total heat transfer to obtain the cumulative heat value for each zone.

[0015] By employing the above technical solution, the temperature decay curve is divided into multiple sampling points with equal time intervals along the time dimension. At each sampling point, the temperature gradient between each zone and its adjacent zones is obtained. Boundary conditions for the heat diffusion equation are established based on the thermal conductivity and geometric dimensions of each zone, allowing the determination of the heat transfer process between the battery pack zones. The temperature gradient is substituted into the heat diffusion equation to calculate the instantaneous heat flux density, and the total heat transfer is obtained through integration along the time dimension. Finally, the initial heat is superimposed to obtain the cumulative heat value. This approach considers the dynamic changes in heat in both time and space, reducing the instantaneous errors that may arise from simple temperature measurements. The solution process of the heat diffusion equation not only reflects the mutual influence of heat between zones but also demonstrates the loss and accumulation effects of heat during the transfer process, improving the accuracy of the battery pack temperature field analysis.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the real-time temperature data of each partition is weighted and calculated with a heat migration correction coefficient to obtain the temperature data after heat migration correction, specifically including: Calculate the temperature gradient between adjacent partitions. The temperature gradient is the ratio of the temperature difference between adjacent partitions to the distance between partitions. When the temperature gradient is greater than the first preset threshold, the adaptive weighting coefficient is set to the first preset value; when the temperature gradient is less than the second preset threshold, the adaptive weighting coefficient is set to the second preset value; when the temperature gradient is between the first preset threshold and the second preset threshold, the adaptive weighting coefficient is set to a third preset value that is greater than the temperature gradient, where the first preset value is greater than the third preset value and the second preset value. The real-time temperature data is multiplied by the first coefficient and the heat migration correction coefficient multiplied by the adaptive weighting coefficient to obtain the temperature data after heat migration correction. The sum of the first coefficient and the adaptive weighting coefficient is 1.

[0017] By adopting the above technical solution, the weighting coefficients are dynamically adjusted according to the magnitude of the temperature gradient between adjacent zones. Larger weighting coefficients are used when the temperature gradient is large, smaller weighting coefficients are used when the temperature gradient is small, and weighting coefficients are allocated proportionally when the temperature gradient is in an intermediate state. This allows for automatic adjustment of the heat migration influence factor based on the unevenness of the temperature distribution within the battery pack, making the temperature data after heat migration correction more consistent with actual heat transfer patterns. By weighting the real-time temperature data with the heat migration correction coefficient, the cumulative effect of heat migration is incorporated, improving the accuracy of temperature field reconstruction and making subsequent heating control more closely aligned with the actual thermodynamic characteristics of the battery pack.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after heating the battery pack with heating power, the method further includes: Collect the output voltage and discharge current of the batteries in each zone, and calculate the actual internal resistance of each zone based on the output voltage and discharge current; When the deviation between the actual internal resistance and the nominal internal resistance exceeds a preset deviation threshold, the corresponding partition is determined to be a low-activity region. The heating unit corresponding to the low-activity area is delayed until the last to be started, and the heating power of the heating unit is increased to the maximum value and the heating duration is extended for a preset time. When the deviation between the actual internal resistance and the nominal internal resistance of the low-activity region is detected to be less than the preset deviation threshold, the heating unit is restored to the initial start-up sequence.

[0019] By employing the above technical solution, the actual internal resistance of each battery zone is calculated through real-time monitoring of its output voltage and discharge current, and compared with the nominal internal resistance. This allows for the timely detection of areas with reduced battery activity. For detected low-activity areas, a compensation strategy is adopted that delays the activation of the corresponding heating unit and increases the heating power. This approach provides a stronger thermal activation effect on the low-activity areas while ensuring the overall stability of the heating process. Once the actual internal resistance of the low-activity areas returns to the normal range, the heating units are restored to their initial activation sequence. This avoids the impact of overheating on battery life, improves battery activity in localized areas, maintains the overall temperature distribution balance of the battery pack, and enhances the charge-discharge performance and lifespan of the battery pack.

[0020] Secondly, embodiments of this application provide an intelligent zone heating system suitable for new energy vehicle batteries. The intelligent zone heating system suitable for new energy vehicle batteries includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the system to perform the method described in the first aspect and any possible implementation of the first aspect.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system, cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product that, when run on a system, causes the system to execute the method described in any possible implementation of the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application provides an intelligent zone heating method for new energy vehicle batteries. A heating efficiency coefficient is constructed by combining the temperature of each zone during driving with the corresponding discharge amount. This coefficient reflects the unit temperature rise capability under the same electrical input and includes the thermal response differences caused by local structural and material variations. Based on this, the geometric center of the region with the largest heating efficiency coefficient is selected as the temperature inertia center, and the inner and outer layer regions are divided accordingly. This ensures that the geometric division is consistent with the thermal inertia distribution, thereby explicitly incorporating the differences in different heat dissipation paths into subsequent parameter calibration. The ratio of the two temperature differences—the ambient temperature measured during the parking phase and the average temperature of the inner and outer layers—is used as a heat dissipation calibration parameter, ensuring that each zone is heated at different locations. The resulting heat dissipation intensity is converted into a measurable correction quantity. Multiplying this by the heating efficiency yields a temperature compensation coefficient that simultaneously characterizes both self-heating capacity and heat loss, thus more directly representing the actual heat input intensity required for a zone to reach its target temperature. Adjacent zones with small differences in compensation coefficients are merged into a unified heating unit, reducing thermal coupling oscillations at the control unit boundary and frequent device start-stop cycles, improving execution stability and energy utilization continuity. When any unit temperature falls below the operating threshold, the power is determined based on the compensation coefficient, and units are activated sequentially from largest to smallest coefficient. This prioritizes limited heating resources for areas with higher heat demand and stronger heat dissipation, while avoiding overheating of areas with low thermal inertia or weak heat dissipation. This improves the accuracy and efficiency of heating control for new energy vehicle batteries.

[0024] 2. This application provides an intelligent zone heating method for new energy vehicle batteries. It obtains the temperature decay curves during two adjacent parking processes and calculates the heat accumulation value of each zone based on the heat diffusion equation, so that residual heat and lateral heat migration are quantified into calculable thermal state quantities, rather than simply relying on instantaneous temperature. The deviation between the heat accumulation value and the battery temperature of each zone constitutes a heat migration correction coefficient, and the real-time temperature is weighted and corrected accordingly, so that the observed value is closer to the effective temperature reflecting the heat generation capacity of the zone itself, reducing misjudgments caused by residual heat penetration from adjacent areas. The corrected temperature data is used in the heat generation efficiency estimation stage, which can maintain the consistency and comparability of the estimation under frequent start-stop and non-uniform heat dissipation conditions, reduce the impact of operating condition switching on coefficient stability, and in urban congestion and low-speed stop-and-go environments, the heating trigger is more accurate, ineffective heating is reduced, and local overheating and temperature lag are suppressed, thereby obtaining a smoother temperature trajectory and a smaller spatial temperature difference under energy-constrained conditions.

[0025] 3. This application provides an intelligent zone heating method suitable for new energy vehicle batteries. It dynamically adjusts weighting coefficients based on the temperature gradient between adjacent zones, using larger weighting coefficients for larger gradients, smaller weighting coefficients for smaller gradients, and proportional weighting coefficients when the temperature gradient is in an intermediate state. This automatically adjusts the heat migration influence factor according to the unevenness of temperature distribution within the battery pack, making the temperature data after heat migration correction more consistent with actual heat transfer patterns. By weighting and combining real-time temperature data with the heat migration correction coefficient, the cumulative effect of heat migration is incorporated, improving the accuracy of temperature field reconstruction and making subsequent heating control more closely aligned with the actual thermodynamic characteristics of the battery pack. Attached Figure Description

[0026] Figure 1 This is a schematic flowchart of an intelligent zone heating method for new energy vehicle batteries, as described in an embodiment of this application.

[0027] Figure 2 This is a flowchart illustrating an adaptive heating control method based on internal resistance monitoring in an embodiment of this application.

[0028] Figure 3 This is a schematic diagram of the physical device structure of an intelligent zoned heating system for new energy vehicle batteries provided in an embodiment of this application. Detailed Implementation

[0029] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0031] The following example is used in conjunction with Figure 1 The present application describes an intelligent zone heating method for new energy vehicle batteries: Please see Figure 1 This is a schematic flowchart of an intelligent zone heating method for new energy vehicle batteries, as described in this application.

[0032] S101. Detect the number of times the vehicle accelerates and decelerates. When the number of times the vehicle accelerates and decelerates exceeds the preset frequency, it is determined that the vehicle is in complex urban conditions. The system monitors vehicle acceleration changes in real time using onboard sensors, specifically accelerometers, speed sensors, or speed signals acquired from the vehicle's CAN bus. The system sets a detection time window, such as 5 or 10 minutes, within which it counts the number of accelerations and decelerations. Acceleration is defined as a positive change in vehicle speed exceeding a preset threshold per unit time, which can be set to 0.5. Deceleration is defined as a negative change in vehicle speed exceeding a preset threshold per unit time, such as a deceleration less than -0.5 m / s². The system accumulates the total number of accelerations and decelerations within the detection time window. When this total exceeds a preset frequency threshold, such as more than 3 acceleration / deceleration operations per minute, the system determines that the vehicle is operating under complex urban conditions. The preset frequency threshold can be dynamically adjusted based on different city traffic conditions, traffic flow at different times, and vehicle type. The preset frequency threshold is typically set to 2-4 times per minute, with the specific value taking into account urban traffic characteristics and driving habits. The acceleration threshold is set to ±0.5 m / s². This value corresponds to slight acceleration and deceleration in daily driving, which is lower than the intensity of emergency braking (usually greater than 4 m / s²) and rapid acceleration (usually greater than 2 m / s²). Therefore, no limit is imposed here. The first technical solution for detecting acceleration and deceleration events involves directly reading signal changes from the accelerator pedal and brake pedal position sensors via the vehicle's ECU (Electronic Control Unit). An acceleration operation is recorded when the accelerator pedal position increases by more than 10% from a standstill and lasts for more than 0.5 seconds; a deceleration operation is recorded when the brake pedal is depressed by more than 15% and lasts for more than 0.5 seconds. The system avoids misjudgments by setting a buffer time, for example, requiring at least a 2-second interval between two valid acceleration or deceleration operations. The second technical solution combines GPS positioning data with an Inertial Measurement Unit (IMU) for comprehensive judgment. The system collects GPS position information once per second, calculates instantaneous velocity based on the position changes of two adjacent sampling points, and then calculates acceleration based on velocity changes. Simultaneously, the three-axis acceleration data provided by the IMU is processed by Kalman filtering and fused with the acceleration calculated from GPS to improve detection accuracy. When the fused acceleration value exceeds a set threshold, the system records the corresponding acceleration / deceleration event.

[0033] S102. Under complex urban conditions, obtain the temperature decay curves of each zone during two adjacent parking processes; After determining that the vehicle is in complex urban conditions, the system begins monitoring the temperature changes in each zone of the battery pack. The battery pack is pre-divided into multiple monitoring zones, each equipped with a temperature sensor, such as a thermocouple or thermistor. The system uses the vehicle speed sensor to determine the vehicle's parking status. When the vehicle speed drops to 0 km / h and remains there for more than a preset time (e.g., 3 seconds), it is considered the start of a parking event. From the moment parking begins, the system records the temperature data of each zone at a fixed sampling frequency (e.g., once per second) until the vehicle restarts. The temperature decay curve is the data sequence of temperature changes in each zone over parking time. The system can use methods such as polynomial fitting, exponential decay models, or piecewise linear interpolation to fit the discrete temperature data points to obtain a continuous temperature decay function. Two adjacent parking sessions refer to the complete driving cycle from the end of one parking session (restart) to the start of the next parking session, which is not limited here.

[0034] The first technical solution for obtaining temperature decay curves is to use a high-precision temperature sensor array for distributed measurement. The system deploys temperature sensors at the geometric center and boundary locations of each battery compartment, forming a three-dimensional temperature field monitoring network. The sensors convert temperature signals into voltage signals via analog signal conditioning circuits, and then into digital signals via analog-to-digital converters (ADCs). The system uses a moving average filtering algorithm to preprocess the raw temperature data to eliminate measurement noise. For each compartment, the system calculates the weighted average temperature of all sensors within that compartment as the representative temperature of that compartment, with the weighting coefficients determined based on the distance of the sensors from the compartment center. The second technical solution is non-contact temperature monitoring based on infrared thermal imaging technology. The system installs a miniature infrared thermal imager inside the battery pack, calculating the temperature distribution by scanning the infrared radiation intensity on the surface of each compartment. The temperature matrix data output by the thermal imager is processed by an image segmentation algorithm to extract the average, highest, and lowest temperatures of each compartment. The system establishes a temperature-time database, uses the least squares method to fit the temperature decay curve, and calculates the goodness-of-fit R² value to evaluate the curve quality.

[0035] S103. Based on the temperature decay curve, the heat accumulation value of each zone is calculated using the heat diffusion equation; The system calculates the cumulative heat value of each zone based on the temperature decay curve and the heat diffusion equation. Specifically, the system divides the temperature decay curve into multiple sampling points with equal time intervals in the time dimension; at each sampling point, it obtains the temperature gradient between each zone and adjacent zones; based on the thermal conductivity and geometric dimensions of each zone, it establishes the boundary conditions of the heat diffusion equation; it substitutes the temperature gradient of each zone at each sampling point into the heat diffusion equation to calculate the instantaneous heat flux density between adjacent zones; it integrates the instantaneous heat flux density in the time dimension to obtain the total heat migration of each zone during the parking process; and it superimposes the initial heat of each zone before parking with the total heat migration to obtain the cumulative heat value of each zone.

[0036] The system utilizes acquired temperature decay curve data to calculate the heat accumulation in each zone during parking by solving the heat diffusion equation. The heat diffusion equation, based on Fourier's law, describes the heat conduction process in the medium. The system first discretizes the continuous temperature decay curve on the time axis, setting a fixed time step Δt (e.g., 0.1 seconds). At each time node ti, the system extracts the temperature value T(i,j) of each zone, where i represents the time index and j represents the zone number. For adjacent zones, the system calculates the temperature gradient ∇T=(T(i,j+1)-T(i,j)) / Δx, where Δx is the distance between zones. The system needs to pre-determine the thermal properties of each zone, including thermal conductivity λ, specific heat capacity c, and density ρ, which can be obtained by consulting battery material handbooks or experimental determination. The boundary conditions consider convective and radiative heat transfer of the battery pack casing, with a heat flux density q=h(T_surface-T_ambient), where h is the overall heat transfer coefficient. The system substitutes the temperature gradient and boundary conditions into the three-dimensional thermal diffusion equation ∂T / ∂t=α∇²T (where α=λ / (ρc) is the thermal diffusivity coefficient), and performs numerical solutions using the finite difference method or the finite element method. By calculating the instantaneous heat flux density q(i,j)=-λ∇T at each time step and integrating ∫qdt over the entire parking period, the total heat transfer between each zone is obtained. Finally, the system adds the initial heat Q_initial (calculated using initial temperature and heat capacity) of each zone before parking to the heat transfer amount to obtain the cumulative heat value Q_total for each zone; this is not limited here.

[0037] The first technical approach to calculating the accumulated heat value is to solve the heat diffusion equation using the explicit finite difference method. The system discretizes the battery pack space into a three-dimensional mesh, with each mesh node corresponding to the center point of a partition. In the time dimension, the system uses a forward difference scheme to approximate the time derivative and a central difference scheme to approximate the spatial second derivative. The specific iterative formula is: T(i+1, j, k, l) = T(i, j, k, l) + αΔt / Δx² [T(i, j+1, k, l) + T(i, j-1, k, l) + T(i, j, k+1, l) + T(i, j, k-1, l) + T(i, j, k, l+1) + T(i, j, k, l-1) - 6T(i, j, k, l)], where j, k, and l represent the spatial indices in the x, y, and z directions, respectively. To ensure numerical stability, the system needs to satisfy the stability condition αΔt / Δx² ≤ 1 / 6. The second technical solution employs the finite element method combined with commercial software for solving the problem. The system imports the CAD model of the battery pack into the finite element analysis software, automatically generating tetrahedral or hexahedral meshes. The system defines material properties, initial conditions, and boundary conditions, and selects the transient thermal analysis module for solving. Internally, the software uses the Galerkin weighted residual method to transform the partial differential equations into a system of algebraic equations, and solves the large sparse matrix using an iterative solver (such as the conjugate gradient method). The system extracts the temperature time history data of each partition node from the solution results, calculates the heat flux vector field through a post-processing module, and then integrates to obtain the accumulated heat value.

[0038] In the process of calculating using the thermal diffusion equation, the system may face the problem of insufficient real-time performance due to excessive computational load. To solve this problem, the system can use a reduced-order model technique to accelerate the calculation. In specific implementation, the system obtains temperature field data under different operating conditions in advance through offline simulation or experiments, and uses the intrinsic orthogonal decomposition (POD) method to extract the main modes of the temperature field. The system represents the high-dimensional temperature field as a linear combination of a few principal modes, and projects the original partial differential equations onto a low-dimensional subspace to obtain a reduced-order ordinary differential equation system.

[0039] S104. The deviation between the accumulated heat value and the battery temperature of each zone is used as the heat migration correction coefficient. After calculating the cumulative heat value for each zone, the system compares it with the actual temperature measurement to obtain the heat migration correction coefficient. First, the system calculates the theoretical temperature T_theory = Q_total / C based on the cumulative heat value Q_total and the heat capacity C of each zone. Then, the system obtains the measured temperature T_actual for each zone at the same time. The temperature deviation is defined as ΔT = T_actual - T_theory, which reflects the difference between the theoretical calculation and the actual situation, possibly due to model simplification, parameter errors, or unconsidered heat sources / heat sinks. The system normalizes the temperature deviation to obtain the dimensionless heat migration correction coefficient k_correction = ΔT / T_ref, where T_ref is the reference temperature, which can be selected as the battery's nominal operating temperature or the average temperature of all zones. The physical meaning of the correction coefficient is to characterize the degree of deviation between the actual thermal behavior of each zone and the theoretical prediction. A positive value indicates that the actual temperature of the zone is higher than the theoretical value, possibly indicating an additional heat source or obstructed heat dissipation; a negative value indicates that the actual temperature is lower than the theoretical value, possibly indicating an additional heat dissipation path. The system can smooth the correction coefficients, such as by using moving average or Kalman filtering, to reduce the impact of measurement noise; however, this is not a limitation.

[0040] The first technical approach to determining the heat migration correction coefficient is parameter identification based on the least squares method. The system establishes a thermal model including the correction coefficient: T_actual = T_theory × (1 + k_correction) + ε, where ε is the measurement error. The system collects temperature data pairs (T_actual, T_theory) at multiple time points and constructs an overdetermined system of equations. The least squares method is used to solve for the correction coefficient value that minimizes the sum of squared residuals: k_correction = argminΣ(T_actual - T_theory × (1 + k))². To improve identification accuracy, the system can use weighted least squares, assigning higher weights to more recent data. The second technical approach is correction coefficient prediction based on machine learning. The system constructs a neural network model, with input features including accumulated heat value, partition location coordinates, temperature difference between adjacent partitions, and ambient temperature, and the output is the correction coefficient. The system trains the network using historical operating data and optimizes the network parameters using the backpropagation algorithm. After training, the system can input current operating conditions in real time and quickly predict the correction coefficient for each partition, exhibiting stronger nonlinear fitting capabilities compared to traditional methods.

[0041] S105. Weight the real-time temperature data of each zone with the heat migration correction coefficient to obtain the temperature data after heat migration correction. The system performs a weighted calculation of the real-time temperature data of each partition and the heat migration correction coefficient to obtain the heat migration corrected temperature data. Specifically, this includes: calculating the temperature gradient between adjacent partitions, where the temperature gradient is the ratio of the temperature difference between adjacent partitions to the partition spacing; when the temperature gradient is greater than a first preset threshold, setting the adaptive weight coefficient to a first preset value; when the temperature gradient is less than a second preset threshold, setting the adaptive weight coefficient to a second preset value; when the temperature gradient is between the first and second preset thresholds, setting the adaptive weight coefficient to a third preset value that is greater than the temperature gradient, where the first preset value is greater than the third preset value, which is greater than the second preset value; and multiplying the real-time temperature data by the first coefficient and the heat migration correction coefficient by the adaptive weight coefficient to obtain the heat migration corrected temperature data, where the sum of the first coefficient and the adaptive weight coefficient is 1.

[0042] After obtaining the heat migration correction coefficient, the system needs to apply it to the real-time temperature data correction process. The system first continuously collects real-time temperature data T_real-time for each partition, with a sampling frequency consistent with the control cycle of the battery management system, typically 10Hz or higher. For each partition j, the system calculates the temperature gradient with all its adjacent partitions. The gradient calculation formula is G(j,k)=|T(j)-T(k)| / d(j,k), where T(j) and T(k) are the temperatures of partition j and the adjacent partition k, respectively, and d(j,k) is the distance between the centers of the two partitions. The system dynamically adjusts the weighting coefficients based on the magnitude of the temperature gradient: when the gradient is greater than a first preset threshold (e.g., 5°C / cm), it indicates significant heat transfer, and the adaptive weighting coefficient w_adaptive is set to a larger first preset value (e.g., 0.8); when the gradient is less than a second preset threshold (e.g., 1°C / cm), heat transfer is weaker, and the weight is set to a smaller second preset value (e.g., 0.2); when the gradient is between the two thresholds, the weights are calculated using linear or nonlinear interpolation, for example, w_adaptive = 0.2 + 0.6 × (G - G_min) / (G_max - G_min). The final corrected temperature calculation formula is T_corrected = (1 - w_adaptive) × T_real-time + w_adaptive × T_real-time × k_correction, ensuring the continuity of temperature before and after correction. The system can perform a rationality check on the corrected temperature to ensure that physically impossible temperature values ​​do not occur; this is not limited here.

[0043] The first technical solution for weighted correction of temperature data is adaptive weight adjustment based on fuzzy logic. The system establishes a fuzzy inference system, with input variables including temperature gradient, gradient rate of change, and correction coefficient magnitude, and outputting adaptive weights. The system defines fuzzy sets, such as dividing temperature gradients into small, medium, and large levels, using triangular or Gaussian membership functions. A fuzzy rule base is established, for example, IF large temperature gradient AND large correction coefficient THEN large weight. Accurate weight values ​​are obtained through fuzzy inference and defuzzification processes. The second technical solution is data fusion based on Kalman filtering. The system treats real-time temperature and corrected temperature as two independent observations, establishing a state-space model. The state vector contains the real temperature, and the observation equations correspond to the two temperature measurements respectively. The system statistically obtains the covariance matrix of process noise and measurement noise based on historical data. Through the prediction-update step of Kalman filtering, the two temperature information are fused to obtain the optimal estimated temperature value. This method can adaptively adjust the reliability of the two data sources and remain stable even when sensor noise is high.

[0044] S106. Use temperature data corrected by heat migration to replace the real-time temperature data of each zone. The system uses thermally migrated corrected temperature data to replace the real-time temperature data for each partition in calculating the thermal efficiency coefficient of each partition. After completing the thermal migration correction of the temperature data, the system needs to integrate the corrected data into the data stream of the battery management system. The system first performs a data integrity check on the corrected temperature data T_corrected to ensure that the temperature values ​​of all partitions are within a reasonable range (e.g., -40°C to 80°C). The system then updates its internal temperature data cache, replacing the original real-time temperature data T_real-time with the corrected data T_corrected. This replacement process must ensure data atomicity to avoid other modules reading inconsistent data during the update process. The system can employ a double-buffering mechanism, maintaining two temperature data arrays: one for current calculations and one for data updates, with pointers swapped after the update. The replaced temperature data will serve as input for all subsequent temperature-related calculations, including thermal efficiency coefficient calculation, safety monitoring, and thermal management decisions. The system can retain a backup of the original temperature data for fault diagnosis or data backtracking analysis; this is not limited here.

[0045] The first technical solution for data replacement is to use a message queue mechanism. The system establishes a temperature data message queue. The correction module encapsulates the corrected temperature data into message packets containing a timestamp, partition ID, and temperature value. These message packets are sent to the data management module via the queue. The data management module processes the messages according to a first-in, first-out (FIFO) principle, updating the temperature data table. This solution decouples data producers and consumers, improving system scalability. The second technical solution is to use a shared memory mechanism. The system allocates a dedicated temperature data shared area in memory, using semaphores or mutexes to protect critical sections. After acquiring a write lock, the correction module directly updates the temperature array in shared memory and releases the lock after the update is complete. Other modules that need to read temperature data access the shared memory through read locks. This solution reduces data copying and improves data transmission efficiency.

[0046] S107. Based on the real-time temperature data and corresponding discharge data of each section of the battery pack during vehicle operation, calculate the heat generation efficiency coefficient of each section. The system calculates the thermal efficiency coefficient of each battery pack zone based on real-time temperature data and corresponding discharge data during vehicle operation. Specifically, this includes: collecting real-time temperature data and ambient temperature data for each battery pack zone within a preset time period during vehicle operation, and calculating the temperature gradient of each zone relative to the ambient temperature; uniformly dividing the preset time period into several time windows, collecting battery discharge current and voltage data within each time window, and calculating the average discharge power within that time window; calculating the rate of change of the temperature gradient for each time window to obtain the instantaneous temperature rise rate of each zone; correlating the instantaneous temperature rise rate with the average discharge power of the corresponding time window to calculate the temperature rise contribution per unit discharge power; calculating the heat transfer loss between adjacent zones to correct the temperature rise contribution; weighting the corrected temperature rise contribution with the actual capacity of the battery pack to obtain a capacity-standardized thermal characteristic value; and using the deviation ratio between the thermal characteristic value of each zone and the preset standard thermal characteristic value as the thermal efficiency coefficient. The thermal efficiency coefficient is the ratio of the temperature rise rate generated under unit power input to the standard value, reflecting the thermal characteristics of the battery zone under the same energy input conditions. This coefficient takes into account the influence of factors such as battery material characteristics, structural layout and heat conduction path on temperature rise. The larger the value, the stronger the heat generation capacity of that zone.

[0047] During vehicle operation, the system needs to comprehensively analyze temperature changes and energy consumption to assess the heat dissipation characteristics of each zone. The system first continuously collects various data within a preset time period (e.g., 5 minutes). Temperature data collection includes the real-time temperature T(t) of each zone and the ambient temperature T_amb(t), calculating the relative temperature rise ΔT(t) = T(t) - T_amb(t). The system divides the time period into multiple time windows (e.g., 30 seconds), collecting the battery discharge current I(t) and terminal voltage V(t) within each window, calculating the average discharge power P_avg = (1 / n)Σ(I(t)×V(t)). For temperature data, the system uses a numerical differential method to calculate the temperature rise rate dT / dt, which can be achieved using the central difference formula: (dT / dt)_i = (T(i+1) - T(i-1)) / (2Δt). The system establishes a mapping relationship between the temperature rise rate and discharge power, calculating the temperature rise contribution per unit power α = (dT / dt) / P_avg, in °C / (s·W). Considering the influence of heat conduction between adjacent zones, the system needs to perform heat loss correction. According to Fourier's law, the heat conduction power from zone j to adjacent zone k is Q(j,k) = λ × A × (T(j) - T(k)) / d, where A is the contact area. The system calculates the net temperature rise contribution α_net = α + ΣQ / (m × c × P_avg), where m is the zone mass and c is the specific heat capacity. Finally, the system standardizes the corrected temperature rise contribution with the actual battery capacity to obtain the capacity-normalized thermal characteristic value β = α_net × C_nominal, where C_nominal is the nominal capacity. The thermal efficiency coefficient is defined as η_thermal = β / β_standard, where β_standard is a preset standard thermal characteristic value, which can be obtained through laboratory calibration or simulation, and is not limited here.

[0048] The first technical approach for calculating the heating efficiency coefficient is based on least squares regression analysis. The system collects temperature rise rate and discharge power data pairs for each time window, constructing a dataset {(P_i, dT / dt_i), i=1, 2, ..., N}. A linear regression model dT / dt=a×P+b is used, where a is the temperature rise contribution coefficient and b is the basic heat generation rate. The system uses the least squares method to solve for a: a=(NΣ(P_i×dT_i)-ΣP_i×ΣdT_i) / (NΣP_i²-(ΣP_i)²). To improve regression accuracy, the system can introduce polynomial regression or piecewise linear regression to capture nonlinear heating characteristics. The second technical approach is online identification based on the recursive least squares (RLS) algorithm. The system initializes the parameter vector θ=[a, b]ᵀ and the covariance matrix P. For each new data sample, the system calculates the Kalman gain K = P × φ / (λ + φᵀ × P × φ), where φ = [P, 1]ᵀ is the regression vector and λ is the forgetting factor. The parameter estimate is updated θ = θ + K × (dT / dt - φᵀ × θ), and the covariance is updated P = (PK × φᵀ × P) / λ. This method can track changes in heating characteristics in real time and adapt to gradual processes such as battery aging.

[0049] S108. Based on the distribution of the heating efficiency coefficients of each zone, the geometric center of the region with the largest heating efficiency coefficient is taken as the temperature inertia center. After obtaining the thermal efficiency coefficients of each zone, the system needs to analyze their spatial distribution characteristics to determine the temperature inertia center. The system first sorts the thermal efficiency coefficients of all zones, identifying the zones with the highest coefficient values ​​(e.g., the top 20%). These high thermal efficiency zones typically correspond to areas in the battery pack with high current density or poor heat dissipation. The system obtains the geometric coordinate information (x_i, y_i, z_i) of these zones, where i represents the zone index. For zones with regular shapes, the geometric center is its centroid; for irregular shapes, the system can calculate it through numerical integration. The system uses a weighted average method to calculate the comprehensive geometric center of the high thermal efficiency region: x_center = Ση_i × x_i / Ση_i. The calculation of y_center and z_center is similar, where η_i is the thermal efficiency coefficient of each zone. This weighted center point is defined as the temperature inertia center, representing the location in the battery pack where heat concentration and temperature change are most significant. The system can verify the reasonableness of the calculation results to ensure that the temperature inertia center is located within the physical boundaries of the battery pack; this is not limited here. The temperature inertia center represents the location in the battery pack where heat accumulation is most significant and the temperature response is slowest. It typically corresponds to the region with the longest heat diffusion path and the worst heat dissipation conditions. Temperature changes at this center point are hysteretic and can serve as a key reference point for assessing the thermal state of the entire battery pack.

[0050] The first approach to determining the temperature inertia center is based on cluster analysis. The system combines the location coordinates and heating efficiency coefficients of each partition into a feature vector F_i=[x_i, y_i, z_i, η_i]. Using the K-means clustering algorithm, all partitions are divided into K clusters (K can be determined using the elbow rule). For the cluster with the highest heating efficiency coefficient, its centroid is calculated as a candidate temperature inertia center. The system evaluates the spatial connectivity of the partitions within this cluster; if the partitions are too dispersed, the weight of spatial distance in the clustering is increased, and the clustering is recalculated. The second approach is based on kernel density estimation. The system defines the heating efficiency density function ρ(x, y, z)=Ση_i×K((x-x_i) / h, (y-y_i) / h, (z-z_i) / h) in three-dimensional space, where K is the kernel function (e.g., a Gaussian kernel) and h is the bandwidth parameter. The maximum point of the density function is found using numerical optimization methods (e.g., gradient ascent), and this point is the temperature inertia center. This method can smoothly process discrete partitioned data to obtain a continuous temperature inertia distribution.

[0051] S109. Using the temperature inertia center as a reference point, the battery pack is divided into an inner layer region and an outer layer region. After determining the temperature inertia center, the system needs to divide the battery pack into regions based on this center point. The system first calculates the Euclidean distance d_i = √[(x_i - x_center)² + (y_i - y_center)² + (z_i - z_center)²] from the center of each region to the temperature inertia center. The system needs to determine the boundary distance d_boundary between the inner and outer layers, which can be achieved using several methods: one is based on statistical distribution, calculating the median or mean of all distances as the boundary; another is based on the physical structure of the battery pack, such as setting the boundary at 1 / 2 or 2 / 3 of the battery pack size. Regions with a distance less than d_boundary are classified as inner regions, and those with a distance greater than or equal to d_boundary are classified as outer regions. The system needs to handle boundary cases; for large regions crossing the boundary line, they can be classified according to their centroid position or volume percentage. After division, the system verifies the connectivity between the inner and outer regions, ensuring that regions within the same layer are spatially continuous or quasi-continuous. For isolated regions, the system can adjust based on the affiliation of their adjacent regions; this is not limited here.

[0052] The first technical solution for region partitioning is based on the Voronoi diagram method. The system constructs Voronoi cells in three-dimensional space using the temperature inertia center as the seed point. Based on the battery pack's geometry and heat dissipation characteristics, the system defines a non-uniform metric function, giving greater weight to distance along the heat dissipation path. The system calculates the weighted distance from each partition to the temperature inertia center, considering the tortuosity of the heat conduction path. By setting a distance threshold, the Voronoi cells are divided into inner and outer regions. The second technical solution is based on the graph cut algorithm. The system constructs a partitioned connection graph, where nodes represent partitions and edges represent adjacency relationships. An energy function is defined as E = Σw_i × f_i + Σw_ij × |f_i - f_j|, where f_i ∈ {0, 1} indicates that partition i belongs to the inner layer (0) or the outer layer (1), w_i is the weight of the data item (related to the distance to the center), and w_ij is the weight of the smoothing term (encouraging adjacent partitions to belong to the same layer). The maximum flow minimum cut algorithm is used to solve for the optimal partitioning, minimizing the energy function.

[0053] S110. Detect the ambient temperature when the vehicle is parked, and calculate the first temperature difference between the average temperature of the inner layer area and the ambient temperature, and the second temperature difference between the average temperature of the outer layer area and the ambient temperature. The system performs ambient temperature detection and temperature difference calculation while the vehicle is stationary. When the vehicle speed drops to 0 and remains there for more than a preset time (e.g., 5 seconds), the system activates the ambient temperature sensor, which is typically located outside the battery pack but in a position protected from direct sunlight and engine heat radiation. The system collects readings from multiple ambient temperature sensors, removes outliers, and takes the average value as the ambient temperature T_env. Simultaneously, the system reads the temperature values ​​{T_inner_i} of all zones in the inner region and calculates the arithmetic mean T_inner_avg = (1 / N_inner)ΣT_inner_i, where N_inner is the number of inner zone zones. Similarly, the system calculates the average temperature of the outer region T_outer_avg = (1 / N_outer)ΣT_outer_i. The first temperature difference is defined as ΔT1 = T_inner_avg - T_env, representing the temperature rise of the inner region relative to the environment; the second temperature difference is defined as ΔT2 = T_outer_avg - T_env, representing the temperature rise of the outer region. The system needs to ensure the synchronization of temperature data; all temperature values ​​should be collected at the same time or within a very short time window. Temperature difference calculation can be performed by averaging multiple samples to improve measurement accuracy; this is not limited here. The first preset threshold can be set to 5°C / cm, and the second preset threshold can be set to 1°C / cm. When the temperature gradient is greater than 5°C / cm, it indicates significant heat accumulation; when the temperature gradient is less than 1°C / cm, it indicates a relatively uniform temperature distribution. These two thresholds are based on the temperature gradient limits for safe operation of lithium-ion batteries; excessively large temperature gradients may lead to stress concentration inside the battery.

[0054] The first technical approach to calculating temperature difference uses a weighted average method to account for volume differences between zones. The system acquires the volume information V_i of each zone and calculates the volume-weighted average temperature: T_inner_avg = ΣV_i × T_i / ΣV_i (inner zone), and T_outer_avg is calculated similarly. This method more accurately reflects the overall thermal state of the region and avoids the excessive influence of temperature anomalies in small zones on the average value. The system can also incorporate the standard deviation information of the temperature distribution; when the temperature distribution of a certain layer is too discrete (the standard deviation exceeds a threshold), the median is used instead of the average value. The second technical approach is surface temperature measurement based on thermal imaging. The system uses an infrared thermal imager to acquire temperature distribution images of the battery pack surface and identifies the boundaries between inner and outer zones through image processing algorithms. The temperature value of each pixel is integrated and divided by the total number of pixels to obtain the average temperature of the region. This method can capture the continuous distribution of the temperature field and identify local hot or cold spots.

[0055] S111, Use the ratio of the first temperature difference to the second temperature difference as the heat dissipation calibration parameter; After obtaining the temperature difference data between the inner and outer layers, the system quantifies the difference in heat dissipation characteristics by calculating the ratio between the two. The heat dissipation calibration parameter is defined as γ = ΔT1 / ΔT2 = (T_inner_avg - T_env) / (T_outer_avg - T_env). This ratio reflects the heat retention capacity of the inner layer relative to the outer layer. When γ > 1, it indicates that the temperature of the inner layer is higher than that of the outer layer, the heat dissipation path is longer, and the thermal resistance is larger; when γ < 1, there may be forced internal heat dissipation or abnormal conditions; when γ ≈ 1, the heat dissipation characteristics of the inner and outer layers are similar. The system needs to handle cases where the denominator is close to zero. When |ΔT2| < ε (ε is a small positive number, such as 0.5°C), γ can be set to γ_default or the historical average value can be used. The physical meaning of the heat dissipation calibration parameter is to quantify the three-dimensional heat dissipation non-uniformity of the battery pack, providing a basis for subsequent heating power allocation. The system can establish a correlation model between the heat dissipation calibration parameter and environmental conditions (such as wind speed and humidity) to achieve more accurate calibration, which is not limited here.

[0056] S112. Multiply the heat generation efficiency coefficient of each zone by the heat dissipation calibration parameters to obtain the temperature compensation coefficient of each zone. The system comprehensively considers the heating characteristics and heat dissipation conditions of each partition to calculate the temperature compensation coefficient. For each partition i, the temperature compensation coefficient is defined as ξ_i = η_i × γ_i, where η_i is the heating efficiency coefficient of the partition, and γ_i is the heat dissipation calibration parameter corresponding to the layer (inner or outer layer) to which the partition belongs. For the inner layer partition, γ_i = γ; for the outer layer partition, since the heat dissipation calibration parameter is defined by the ratio of the inner and outer layers, the system needs to perform normalization processing, which can be done by setting γ_outer = 1, then γ_inner = γ. The physical meaning of the temperature compensation coefficient is to comprehensively reflect the relative heating intensity required for the partition to reach the target temperature. The larger the value, the stronger the heat dissipation or the higher the heating efficiency of the partition, requiring more heating power compensation. The system standardizes the calculated temperature compensation coefficient, which can be done by maximum value normalization ξ_norm = ξ_i / max(ξ) or Z-score normalization, so that the coefficient is distributed within a suitable numerical range, which is convenient for the implementation of subsequent control algorithms. No limitation is made here.

[0057] The first approach to calculating the temperature compensation coefficient considers the correction for the boundary effect of the partition. The system identifies boundary partitions located at the interface between the inner and outer layers, whose heat dissipation characteristics fall between those of a pure inner layer and a pure outer layer. The system calculates the contact area ratios r_inner and r_outer (r_inner + r_outer = 1) between the boundary partition and the inner and outer layers. The equivalent heat dissipation calibration parameter for the boundary partition is γ_boundary = r_inner × γ_inner + r_outer × γ_outer. The system uses the corrected heat dissipation parameters to calculate the temperature compensation coefficient for the boundary partition, achieving a smooth transition. The second approach introduces a second-order correction for the temperature gradient. The system considers not only the average heat dissipation characteristics of the layer to which the partition belongs but also the influence of the local temperature gradient. The system calculates the average temperature gradient ∇T_i between partition i and all its neighboring partitions, defining a gradient correction factor f_grad = 1 + α × ∇T_i / ∇T_avg, where α is the correction intensity coefficient. The corrected temperature compensation coefficient is ξ_i=η_i×γ_i×f_grad, which can more accurately reflect the differences in local heat dissipation.

[0058] S113. Merge adjacent zones with a temperature compensation coefficient difference less than a preset threshold into the same heating unit; The system aggregates partitions based on the similarity of their temperature compensation coefficients to form heating control units. First, the system establishes a partition adjacency graph, identifying all adjacent partitions for each partition. For any two adjacent partitions i and j, the difference in their temperature compensation coefficients, Δξ_ij = |ξ_i - ξ_j|, is calculated. The system sets a difference threshold ε_merge (e.g., 0.05 or 5% of the maximum coefficient). When Δξ_ij < ε_merge, the two partitions are considered mergeable. The merging process uses a region growing algorithm: a seed partition is selected, and all adjacent partitions meeting the merging criteria are added to the same set. The neighbors of newly added partitions are recursively processed until no new partitions can be added. The system handles merging conflicts, where a partition may meet the merging criteria with multiple unconnected partitions. In this case, the partition with the smallest difference is merged first. After merging, each heating unit contains one or more original partitions. The system assigns a unique identifier to each unit and calculates the unit's comprehensive temperature compensation coefficient (e.g., by averaging or weighted averaging). The purpose of the merging strategy is to reduce the number of independently controlled heaters, thereby lowering system complexity and cost, while avoiding energy waste caused by differentiated control of zones with similar characteristics. No specific limitations are set here. The preset threshold for the temperature compensation coefficient difference is typically set between 0.03 and 0.08. A setting of 0.05 means that adjacent zones can be merged when their temperature compensation coefficients differ by no more than 5%. The selection of this threshold requires a trade-off between control accuracy and system complexity: too small a threshold may lead to excessive zoning, increasing control costs; too large a threshold may ignore local differences, affecting control performance. Specific settings can be adjusted according to the battery pack size, the number of zones, and temperature uniformity requirements.

[0059] The first technical solution for partition merging is based on a hierarchical clustering algorithm. The system constructs an initial clustering tree, with each partition as an independent class. The inter-class distance is defined as the weighted sum of the temperature compensation coefficient difference and the spatial distance: d_cluster=w1×|ξ_i-ξ_j|+w2×d_spatial. The system adopts a bottom-up merging strategy, merging the two classes with the smallest distance each time and updating the inter-class distance matrix. Merging stops when the minimum inter-class distance exceeds a threshold. This method can generate clustering results of different granularities, and the system can select an appropriate truncation height according to actual needs. The second technical solution is a community detection algorithm based on graph segmentation. The system constructs a weighted undirected graph, with nodes as partitions and edge weights of w_ij=exp(-|ξ_i-ξ_j| / σ)×I_adjacent, where I_adjacent is the adjacency indicator function and σ is the scale parameter. The Louvain algorithm or spectral clustering algorithm is used to detect community structures in the graph, with each community corresponding to a heating unit. The optimization objective is to maximize the modularity Q=Σ(e_ii-a_i²), where e_ii is the weight ratio of edges within a community and a_i is the weight ratio of edges connected to community i.

[0060] S114. When it is detected that the current temperature of any heating unit is lower than the preset working temperature, determine the heating power according to the temperature compensation coefficient of the heating unit. When it is detected that the current temperature of any heating unit is lower than the preset working temperature, the system determines the heating power according to the temperature compensation coefficient of the heating unit. Specifically, the system obtains the temperature difference between the current temperature and the preset working temperature of each heating unit; based on the temperature compensation coefficient of each heating unit, calculates the equivalent temperature difference after temperature compensation; according to the total power limit of the battery pack and the volume of each heating unit, calculates the maximum heating power that can be allocated per unit volume; converts the equivalent temperature difference into a standardized temperature difference coefficient, where the standardized temperature difference coefficient is the ratio of the equivalent temperature difference of each heating unit to the maximum equivalent temperature difference; based on the standardized temperature difference coefficient, allocates the maximum heating power that can be allocated per unit volume to obtain the heating power of each heating unit.

[0061] The system continuously monitors the temperature status of each heating unit and starts the power calculation program when heating is required. The system first obtains the current temperature of all partitions within each heating unit and calculates the unit average temperature T_unit_current. The preset working temperature T_work is determined according to the battery type and application scenario. For example, for lithium-ion batteries, the optimal working temperature range is 15 - 35°C, and the system can set T_work = 20°C. When T_unit_current < T_work, calculate the temperature difference ΔT_unit = T_work - T_unit_current. The system calculates the equivalent temperature difference ΔT_eq = ΔT_unit × ξ_unit based on the temperature compensation coefficient ξ_unit. This equivalent temperature difference takes into account the heating and heat dissipation characteristics of the unit. The system obtains the total power limit P_total_max of the battery pack and the volume V_unit of each heating unit, and calculates the reference power P_base = P_total_max / Σ_V_unit that can be allocated per unit volume. Convert the equivalent temperature difference into a standardized coefficient κ_unit = ΔT_eq / max(ΔT_eq) to ensure that the coefficients of all units are within the range of [0, 1]. The heating power calculation formula is P_unit = P_base × V_unit × κ_unit × f_adjust, where f_adjust is a dynamic adjustment factor that can be determined according to factors such as battery SOC and ambient temperature. The system needs to ensure that the total power of all units does not exceed P_total_max. If it exceeds, it will be scaled down proportionally, which is not specified here.

[0062] The first technical solution for determining heating power is dynamic power regulation based on PID control. The system configures an independent PID controller for each heating unit, with the input being the temperature error e(t) = T_work - T_unit_current, and the output being the heating power. The PID parameters are adaptively adjusted according to the unit's temperature compensation coefficient: Kp = Kp_base × ξ_unit, and Ki and Kd are similar. The system implements an anti-integral saturation strategy, stopping integral accumulation when the power reaches the upper or lower limits. A derivative explicit structure is introduced to reduce overshoot; the derivative term only acts on the temperature feedback, not the setpoint. The second technical solution is optimized power allocation based on model predictive control (MPC). The system establishes a state-space model of the heating unit, with temperature as the state variable and heating power as the control variable. In each control cycle, the system predicts the temperature trajectory for the next N steps, optimizing the objective function J = Σ(T_pred - T_work)² + λΣP², where λ is the power penalty coefficient. Considering power constraints and temperature rise rate constraints, quadratic programming is used to solve for the optimal power sequence, and the first value is taken as the current control variable.

[0063] S115. Start the heaters of each heating unit in descending order of temperature compensation coefficient, and control the heating of the battery pack with heating power.

[0064] After determining the power of each heating unit, the system needs to formulate a heater startup strategy and execute heating control. The system first sorts all units requiring heating in descending order of their temperature compensation coefficient ξ_unit, generating a startup priority list. Units with larger temperature compensation coefficients indicate stronger heat dissipation or higher heating efficiency and should be prioritized for heating to prevent further temperature drops. The system sets a startup interval Δt_start (e.g., 2 seconds) to avoid power surges caused by simultaneously starting multiple high-power heaters. For the i-th unit, the startup time is t_start_i = (i-1) × Δt_start. The system sends startup commands and power setpoints to the controllers of each heating unit. The heaters adjust their power through pulse width modulation (PWM) or silicon controlled rectifier (SCR) voltage regulation. During heating, the system continuously monitors the temperature changes of each unit and adjusts the heating power in real time. When the temperature of a unit reaches T_work + ΔT_hysteresis (hysteresis temperature, e.g., 2°C), the system reduces the power of that unit or shuts down the heater to prevent overheating. The system records heating process data, including temperature rise curves, power consumption, and heating time of each unit, which are used to evaluate the heating effect and optimize control parameters. No specific limitations are imposed here.

[0065] The first technical solution for implementing heating control is hierarchical heating management based on a state machine. The system defines multiple operating states for each heating unit: standby, preheating, rapid heating, constant temperature maintenance, and cooling protection. State transition conditions are based on temperature and the rate of temperature change. During the preheating phase, the system soft-starts at 30% of rated power to avoid thermal shock; during the rapid heating phase, it uses the calculated target power; when approaching the target temperature, it switches to constant temperature maintenance, reducing the power to 10%–20%. The system implements fault detection; if an abnormal temperature rise rate (too fast or too slow) is detected, it automatically switches to a safe mode. The second technical solution is heating control based on distributed collaboration. The system configures a local controller for each heating unit, possessing temperature acquisition, power control, and communication functions. The central controller broadcasts global temperature targets and power limits, and local controllers make autonomous decisions based on their own state and information from neighboring units. A consensus algorithm is used to ensure that the temperature difference between adjacent units does not exceed the allowable range, avoiding thermal stress. Local controllers exchange temperature and power information via a CAN bus, achieving decentralized collaborative control.

[0066] In the above embodiments, a heating efficiency coefficient is constructed by combining the temperature of each zone during driving with the corresponding discharge amount. This coefficient reflects the unit temperature rise capability under the same electrical power input and includes the thermal response differences caused by local structural and material variations. Based on this, the geometric center of the region with the largest heating efficiency coefficient is selected as the temperature inertia center, and the inner and outer layer regions are divided accordingly. This ensures that the geometric division is consistent with the thermal inertia distribution, thereby explicitly incorporating the differences in different heat dissipation paths into subsequent parameter calibration. The ratio of the two sets of temperature differences between the ambient temperature and the average temperature of the inner and outer layers measured during the parking phase is used as a heat dissipation calibration parameter, transforming the heat dissipation intensity caused by each zone at different locations into a quantifiable value. The temperature compensation coefficient, obtained by multiplying the measured correction amount by the heating efficiency, simultaneously characterizes both self-heating capacity and heat dissipation loss, thus more directly representing the actual heat input intensity required for a zone to reach its target temperature. Adjacent zones with small differences in compensation coefficients are merged into a unified heating unit, reducing thermal coupling oscillations at the control unit boundary and frequent device start-stop cycles, improving execution stability and energy utilization continuity. When any unit temperature is below the operating threshold, the power is determined based on the compensation coefficient, and units are started sequentially from largest to smallest coefficient. This prioritizes limited heating resources for areas with higher heat demand and stronger heat dissipation, while avoiding overheating of areas with low thermal inertia or weak heat dissipation. This improves the accuracy and efficiency of heating control for new energy vehicle batteries.

[0067] The partitioned heating method described in the above embodiments achieves precise heating control based on the thermal efficiency coefficient and heat dissipation calibration parameters. However, in practical applications, different partitions of the battery may exhibit activity differences due to factors such as material properties, charge / discharge history, or local stress. These differences can affect the uniformity of the heating effect and may even lead to local overheating or underheating. Therefore, this application also provides an adaptive heating control method based on internal resistance monitoring. By evaluating the electrochemical activity state of each partition in real time, the heating strategy is dynamically adjusted to achieve precise thermal activation of low-activity regions. The following section combines... Figure 2 The following describes an adaptive heating control method based on internal resistance monitoring in an embodiment of this application: Please see Figure 2 This is a flowchart illustrating an adaptive heating control method based on internal resistance monitoring in an embodiment of this application.

[0068] S201. Collect the output voltage and discharge current of the battery in each zone, and calculate the actual internal resistance of each zone based on the output voltage and discharge current; The system acquires battery output parameters in real time using voltage and current sensors configured in each zone. The voltage sensors typically employ high-precision differential amplifier circuits, accurately measuring the terminal voltage of the battery in each zone with an accuracy of ±0.1% or higher. The current sensors, such as Hall effect current sensors or shunts, are placed in the main circuit of each zone to monitor the magnitude and direction of the discharge current in real time. The system sets a sampling frequency, typically from 100Hz to 1kHz, to ensure the capture of dynamic changes in current and voltage. During acquisition, the system filters the raw data to remove high-frequency noise and interference signals. The actual internal resistance is calculated based on an extended form of Ohm's law. The system records voltage-current data pairs (V1, I1) and (V2, I2) at different times during discharge and calculates the internal resistance using the differential method: R = |V2 - V1| / |I2 - I1|. To improve calculation accuracy, the system can acquire multiple sets of data for linear regression analysis; the slope represents the internal resistance value. The calculation process needs to consider the battery polarization effect. The system can perform measurements at different discharge rates to obtain dynamic internal resistance characteristic curves. The internal resistance value will change with factors such as temperature, state of charge (SOC), and aging degree. The system needs to record these environmental parameters for subsequent analysis, but this is not limited here.

[0069] The first technical solution for calculating internal resistance is direct measurement based on pulse discharge. The system controls each zone to perform short-duration pulse discharge, with a pulse width set to 0.1-1 seconds and a discharge current of 0.5C-2C of the rated capacity. The system records the open-circuit voltage V0 before the pulse begins and the load voltage V_load and discharge current I_pulse after the pulse stabilizes. The internal resistance is calculated using the formula R=(V0-V_load) / I_pulse. To eliminate polarization effects, the system waits for a certain period (e.g., 10 seconds) after the pulse ends to allow the battery to return to equilibrium before performing the next measurement. The reliability of the results is improved by averaging multiple measurements. The second technical solution is frequency domain analysis based on electrochemical impedance spectroscopy (EIS). The system applies a small-amplitude sinusoidal current excitation (within 5% of the rated current) to each zone's battery, with a frequency range from 0.01Hz to 10kHz. By measuring the amplitude and phase of the voltage response, the complex impedance Z(ω)=V(ω) / I(ω) is calculated. The system uses an equivalent circuit model to fit the impedance spectrum data, where the ohmic internal resistance corresponds to the real intercept in the high-frequency range. This method can separate the ohmic internal resistance, charge transfer resistance, and diffusion impedance, providing more detailed information about the battery's internal structure.

[0070] S202. When the deviation between the actual internal resistance and the nominal internal resistance exceeds the preset deviation threshold, the corresponding partition is determined to be a low-activity region. After obtaining the actual internal resistance of each zone, the system needs to compare it with the nominal internal resistance to identify low-activity areas. The nominal internal resistance is the design internal resistance value of the battery under standard conditions (e.g., 25°C, 50% SOC), usually provided by the battery manufacturer or obtained through initial testing of new batteries. The system calculates the internal resistance deviation rate: δ = (R_actual - R_nominal) / R_nominal × 100%. The preset deviation threshold is determined based on the battery type and application requirements, generally set at 20%-50%. When the internal resistance deviation rate δ of a zone exceeds the threshold, the system marks that zone as a low-activity area. Low activity can be caused by various reasons, including electrolyte decomposition, active material shedding, separator aging, and localized overcharging and over-discharging. The system needs to distinguish between temporary low activity (e.g., caused by low temperature) and permanent degradation, which can be determined through temperature compensation and historical trend analysis. For boundary cases (deviation close to the threshold), the system can set a hysteresis interval to avoid frequent state switching; this is not limited here. The preset threshold for internal resistance deviation is generally set at 20%-50%. When the battery is at room temperature (25±5°C), an internal resistance deviation exceeding 20% ​​typically indicates a slight decrease in activity; a deviation exceeding 35% indicates a significant decrease in activity; and a deviation exceeding 50% may indicate severe degradation. These thresholds are based on studies of the correlation between battery capacity decay and increased internal resistance.

[0071] The first technical approach for identifying low-activity regions is an anomaly detection method based on Statistical Process Control (SPC). The system establishes a control chart for the internal resistance of each partition and calculates the mean μ and standard deviation σ of historical data. A control limit is set: the upper control limit UCL = R_nominal × (1 + k × σ / μ), where k is the control coefficient (usually 3). When m consecutive data points exceed the control limit or show a clear trend, it is determined to be a low-activity state. The system can also use CUSUM (cumulative sum) or EWMA (exponentially weighted moving average) algorithms to improve the sensitivity to detecting slow changes. The second technical approach is a pattern recognition method based on machine learning. The system constructs a Support Vector Machine (SVM) or Random Forest classifier, with input features including internal resistance value, rate of change of internal resistance, temperature, SOC, and cumulative charge / discharge cycles. The model is trained using historical labeled data to distinguish between normal, mildly low-activity, and severely low-activity states. The system can use Principal Component Analysis (PCA) to reduce feature dimensionality and improve classification efficiency. The model outputs a low activity probability, and when the probability exceeds a set threshold (such as 0.8), it is identified as a low activity region.

[0072] S203, Delay the start of the heating unit corresponding to the low-activity area until the last one, and increase the heating power of the heating unit to the maximum value and extend the heating duration of the preset time; After identifying a low-activity region, the system needs to adjust the control strategy of the corresponding heating unit. First, the system modifies the heating start-up sequence, removing the heating unit corresponding to the low-activity region from the original start-up order and placing it at the end of the start-up queue. This adjustment takes into account that the low-activity region requires a longer time and higher temperature to recover its activity, avoiding impacting the heating process of other normal regions. The system obtains the maximum allowable power P_max of the heating unit, a value determined by the heater's rated power, heat dissipation capacity, and safety limits. When starting the heating unit in the low-activity region, the system directly sets the power to P_max, instead of the conventional power calculated based on the temperature compensation coefficient. The preset extension duration Δt_extend is determined based on the degree of low activity and can be set to 1.5-2 times the normal heating time, or a fixed extension of 5-10 minutes. During the extended heating period, the system continuously monitors the temperature rise and internal resistance changes to ensure that the temperature does not exceed the safe upper temperature limit (e.g., 60°C). During heating, the system can employ closed-loop temperature control, temporarily reducing the power when the temperature approaches the upper limit to prevent thermal runaway; this is not limited here. The preset extension time is typically set to 1.5-2 times the original heating time, or fixed at 5-10 minutes. For example, if the heating time for a normal zone is 5 minutes, the heating time for the low-activity zone can be extended to 7.5-10 minutes. The specific setting of the extension time needs to consider the battery's thermal capacity and target temperature. The upper limit of the safe temperature is set at 60°C, which is based on the maximum allowable operating temperature of most lithium-ion batteries.

[0073] The first technical solution for implementing delayed start-up and power boost is a hierarchical control strategy based on finite state machines. The system defines a specific set of states for the low-activity heating unit: waiting, preheating activation, high-power heating, activity recovery assessment, and normal maintenance. In the waiting state, the unit remains off until other units complete basic heating. During the preheating activation phase, it operates at 50% maximum power for 2 minutes to homogenize the battery's internal temperature. The high-power heating phase applies maximum power, with the duration dynamically adjusted based on real-time internal resistance feedback. The system assesses activity recovery every 30 seconds, transitioning to normal maintenance when the internal resistance drops below a threshold. The second technical solution is an optimization control method based on model prediction. The system establishes an electro-thermal-chemical coupling model for the low-activity region, describing the relationship between temperature, internal resistance, and electrochemical reaction rate. An extended Kalman filter is used to estimate unmeasurable states (such as electrolyte concentration distribution). In each control cycle, the system predicts the internal resistance evolution trajectory for the next N steps, optimizing the heating power sequence to minimize the internal resistance recovery time while satisfying temperature constraints and energy consumption limits. The optimal power trajectory obtained may include variable power phases, making it more efficient than constant maximum power.

[0074] S204. When the deviation between the actual internal resistance and the nominal internal resistance of the low-activity region is detected to be less than the preset deviation threshold, the heating unit is restored to the initial start-up sequence.

[0075] During the special heating treatment of the low-activity region, the system needs to continuously evaluate its activity recovery. The system remeasures the actual internal resistance of the low-activity region according to a set detection cycle (e.g., every 60 seconds), using the same measurement method as S201 to ensure data consistency. The system calculates the new internal resistance deviation rate δ_new = (R_actual_new - R_nominal) / R_nominal × 100% and compares it with a preset deviation threshold. Considering measurement fluctuations, the system may require the deviation rate to be below the threshold for n consecutive measurements (e.g., 3 times) before confirming activity recovery. Once activity recovery is confirmed, the system needs to restore the control parameters of the heating unit to their initial settings. This includes: adjusting the heating power from its maximum value back to the normal value calculated based on the temperature compensation coefficient; canceling the extended heating time setting; and re-inserting the unit into its original starting position. The recovery process should be smooth to avoid sudden power changes impacting the battery. The system records the time and energy consumption required for activity recovery for optimizing subsequent heating strategies; this is not limited here.

[0076] The first technical solution for restoring heating units is a smooth transition method based on gradual parameter adjustment. The system does not immediately reduce the power from its maximum value to its normal value, but rather gradually reduces it according to a preset slope, such as a 10% reduction every 10 seconds. Simultaneously, the system monitors the temperature change rate to ensure that the temperature does not drop rapidly due to power reduction. For the restoration of the startup sequence, the system maintains a priority scoring table, calculating the priority score of each unit based on the temperature compensation coefficient and historical performance. The restored unit is inserted into the appropriate position according to its score, rather than simply returning to its original position. The second technical solution is an intelligent switching strategy based on adaptive control. The system uses a fuzzy logic controller to manage the restoration process. Input variables include the internal resistance deviation improvement rate, current temperature, and temperature uniformity. Fuzzy rules define restoration strategies for different situations, such as IF rapid internal resistance improvement AND moderate temperature THEN rapid restoration. The controller outputs a restoration speed coefficient and a target power value, enabling flexible adjustment based on actual conditions. The system can learn from historical restoration cases and continuously optimize the fuzzy rule base.

[0077] In the above embodiments, the actual internal resistance is calculated by real-time monitoring of the output voltage and discharge current of each battery zone and compared with the nominal internal resistance, which can promptly identify areas where battery activity has decreased. For detected low-activity areas, a compensation strategy is adopted to delay the start-up of the corresponding heating unit and increase the heating power. This can apply a stronger thermal activation effect to the low-activity areas while ensuring the stability of the overall heating process. When the actual internal resistance of the low-activity areas returns to the normal range, the heating units are restored to their initial start-up sequence, avoiding the impact of overheating on battery life. This improves the battery activity in local areas, maintains the uniformity of the overall temperature distribution of the battery pack, and improves the charge-discharge performance and service life of the battery pack.

[0078] The system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of an intelligent zoned heating system for new energy vehicle batteries provided in an embodiment of this application.

[0079] It should be noted that, Figure 3 The structure of the system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0080] like Figure 3As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0081] The following components are connected to I / O interface 305: input section 306 including a camera, infrared sensor, etc.; output section 307 including a liquid crystal display (LCD) and speakers, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0082] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0083] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0085] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or it may exist independently and not assembled into the system. The storage medium carries one or more computer programs that, when executed by a processor of a system, cause the system to implement the methods provided in the above embodiments.

[0086] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0087] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0088] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0089] 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. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A smart zone heating method suitable for new energy vehicle batteries, characterized in that, include: Based on the real-time temperature data and corresponding discharge data of each section of the battery pack during vehicle operation, the heat efficiency coefficient of each section is calculated. Based on the distribution of the heating efficiency coefficients of each zone, the geometric center of the region with the largest heating efficiency coefficient is taken as the temperature inertia center. Using the temperature inertia center as a reference point, the battery pack is divided into an inner layer region and an outer layer region; Detect the ambient temperature when the vehicle is parked, and calculate the first temperature difference between the average temperature of the inner layer region and the ambient temperature, and the second temperature difference between the average temperature of the outer layer region and the ambient temperature. The ratio of the first temperature difference to the second temperature difference is used as the heat dissipation calibration parameter; Multiply the heat dissipation efficiency coefficient of each partition by the heat dissipation calibration parameter to obtain the temperature compensation coefficient of each partition; Adjacent zones whose temperature compensation coefficient difference is less than a preset threshold are merged into the same heating unit; If the current temperature of any of the heating units is detected to be lower than the preset operating temperature, the heating power is determined according to the temperature compensation coefficient of the heating unit. The heaters of each heating unit are activated sequentially in descending order of the temperature compensation coefficient, and the battery pack is heated and controlled by the heating power.

2. The method according to claim 1, characterized in that, The calculation of the heat dissipation efficiency coefficient for each section of the battery pack, based on real-time temperature data and corresponding discharge data of each section during vehicle operation, specifically includes: Real-time temperature data of each zone of the battery pack and ambient temperature data are collected during a preset time period when the vehicle is in motion, and the temperature gradient of each zone relative to the ambient temperature is calculated. The preset time period is evenly divided into several time windows. Battery discharge current and voltage data are collected in each time window, and the average discharge power in the time window is calculated. The rate of change of the temperature gradient is calculated for each time window to obtain the instantaneous temperature rise rate of each partition; The instantaneous temperature rise rate is correlated with the average discharge power of the corresponding time window to calculate the temperature rise contribution value per unit discharge power. Calculate the heat transfer loss between adjacent zones to correct the temperature rise contribution value; The corrected temperature rise contribution value is weighted with the actual capacity of the battery pack to obtain the capacity-standardized heat generation characteristic value. The deviation ratio between the heating characteristic value of each zone and the preset standard heating characteristic value is used as the heating efficiency coefficient.

3. The method according to claim 1, characterized in that, The step of determining the heating power based on the temperature compensation coefficient of the heating unit specifically includes: Obtain the temperature difference between the current temperature and the preset operating temperature of each heating unit; Based on the temperature compensation coefficient of each heating unit, the equivalent temperature difference after temperature compensation is calculated. Calculate the maximum heating power that can be allocated per unit volume based on the total power limit of the battery pack and the volume of each heating unit; The equivalent temperature difference is converted into a standardized temperature difference coefficient, which is the ratio of the equivalent temperature difference of each heating unit to the maximum equivalent temperature difference. Based on the standardized temperature difference coefficient, the maximum heating power that can be allocated per unit volume is distributed to obtain the heating power of each heating unit.

4. The method according to claim 1, characterized in that, Before calculating the heat efficiency coefficient of each zone, the method further includes: The number of times a vehicle accelerates and decelerates is detected. When the number of accelerations and decelerations exceeds a preset frequency, it is determined that the vehicle is in complex urban conditions. Under the complex urban conditions, the temperature decay curves of each zone during two adjacent parking processes were obtained; Based on the temperature decay curve, the cumulative heat value of each zone is calculated using the heat diffusion equation; The deviation between the accumulated heat value and the battery temperature of each zone is used as the heat migration correction coefficient; The real-time temperature data of each partition is weighted and calculated with the heat migration correction coefficient to obtain the temperature data after heat migration correction. The temperature data corrected for heat migration is used to replace the real-time temperature data of each partition, and is used to calculate the heating efficiency coefficient of each partition.

5. The method according to claim 4, characterized in that, The calculation of the cumulative heat value of each zone based on the temperature decay curve and the heat diffusion equation specifically includes: The temperature decay curve is divided into multiple sampling points with equal time intervals in the time dimension; At each sampling point, the temperature gradient between each partition and its adjacent partitions is obtained; Based on the thermal conductivity and geometric dimensions of each zone, the boundary conditions of the heat diffusion equation are established. Substitute the temperature gradient of each partition at each sampling point into the heat diffusion equation to calculate the instantaneous heat flux density between adjacent partitions; The instantaneous heat flux density is integrated over time to obtain the total heat transfer of each zone during the parking process; The initial heat before parking in each zone is added together with the total heat migration to obtain the cumulative heat value for each zone.

6. The method according to claim 4, characterized in that, The step of weighting the real-time temperature data of each partition with the heat migration correction coefficient to obtain the heat migration corrected temperature data specifically includes: Calculate the temperature gradient between adjacent partitions, where the temperature gradient is the ratio of the temperature difference between adjacent partitions to the distance between partitions; When the temperature gradient is greater than a first preset threshold, the adaptive weighting coefficient is set to a first preset value; when the temperature gradient is less than a second preset threshold, the adaptive weighting coefficient is set to a second preset value; when the temperature gradient is between the first preset threshold and the second preset threshold, the adaptive weighting coefficient is set to a third preset value that is greater than the temperature gradient, wherein the first preset value is greater than the third preset value and the second preset value is greater than the second preset value. The real-time temperature data is multiplied by a first coefficient, and the sum of the heat migration correction coefficient and the adaptive weighting coefficient is used to obtain the temperature data after heat migration correction. The sum of the first coefficient and the adaptive weighting coefficient is 1.

7. The method according to claim 1, characterized in that, After controlling the heating of the battery pack with the stated heating power, the method further includes: Collect the output voltage and discharge current of the battery in each zone, and calculate the actual internal resistance of each zone based on the output voltage and discharge current; When the deviation between the actual internal resistance and the nominal internal resistance exceeds a preset deviation threshold, the corresponding partition is determined to be a low-activity region. The heating unit corresponding to the low-activity region is delayed until the last to be started, and the heating power of the heating unit is increased to the maximum value and the heating duration is extended for a preset time. When the deviation between the actual internal resistance and the nominal internal resistance of the low-activity region is detected to be less than the preset deviation threshold, the heating unit is restored to the initial startup sequence.

8. A smart zone heating system suitable for new energy vehicle batteries, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the system, the system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the system, the system performs the method as described in any one of claims 1-7.

Images