A battery pulse heating method, device, vehicle and storage medium

Abstract

The application provides a battery pulse heating method and device, a vehicle and a storage medium, and relates to the technical field of battery control. The method comprises the following steps: a first mapping relationship between pulse excitation parameters and thermal response characteristic parameters of a battery is established; a constraint condition between the thermal response characteristic parameters and electrochemical parameters is determined; and a target optimization model is constructed according to the first mapping relationship and the constraint condition, wherein the target optimization model is used for determining actual pulse excitation parameters based on obtained SOC, battery temperature and battery internal resistance; and the pulse excitation parameters are reasonably adjusted to improve heating efficiency.

Description

Technical Field

[0001] This invention relates to the field of battery control technology, and more specifically, to a battery pulse heating method, apparatus, vehicle, and storage medium. Background Technology

[0002] With the development of new energy vehicles, the installed capacity of power batteries is increasing. However, power batteries have a narrow operating temperature range, and excessively high or low temperatures can lead to significant performance degradation or even irreversible damage. For example, when power batteries are charged at low temperatures, they are prone to limited charging current and slower charging speed.

[0003] In related technologies, improving battery charge and discharge performance at low temperatures often involves battery heating methods, including internal pulse heating and external heating. Internal pulse heating, however, is difficult to set appropriately for pulse excitation parameters at low temperatures or low battery levels due to battery characteristics, easily leading to low efficiency or damage to the battery due to improper parameter settings. External heating, proceeding from the outside in, results in uneven temperature distribution and insufficient efficiency. Therefore, how to rationally adjust pulse excitation parameters to improve heating efficiency has become an urgent technical problem to be solved. Summary of the Invention

[0004] The problem addressed by this invention is how to rationally adjust the pulse excitation parameters to improve heating efficiency.

[0005] To address the above problems, the present invention provides a battery pulse heating method, apparatus, vehicle, and storage medium.

[0006] In a first aspect, the present invention provides a battery pulse heating method, comprising:

[0007] Establish the first mapping relationship between the pulse excitation parameters and the thermal response characteristic parameters of the battery;

[0008] Determine the constraints between the thermal response characteristic parameters and the electrochemical parameters;

[0009] A target optimization model is constructed based on the first mapping relationship and the constraints, wherein the target optimization model is used to determine the actual pulse excitation parameters based on the obtained SOC, battery temperature and battery internal resistance.

[0010] Optionally, establishing the first mapping relationship between the pulse excitation parameters and the thermal response characteristic parameters of the battery includes:

[0011] Based on the influence of pulse frequency and amplitude on the heat generation efficiency and temperature distribution inside the battery cell, the first mapping relationship is established, wherein the pulse excitation parameters include the pulse frequency and the amplitude, and the thermal response characteristic parameters include the heat generation efficiency and the temperature distribution.

[0012] Optionally, establishing the first mapping relationship based on the influence of pulse frequency and amplitude on the heat generation efficiency and temperature distribution inside the battery cell includes:

[0013] The range of variation of the cell's internal resistance is divided to obtain internal resistance sub-ranges;

[0014] The corresponding initial temperature range and initial SOC range of the battery cell are determined based on the internal resistance range.

[0015] The first mapping relationship is established under the constraints of the initial temperature range of the battery cell and the initial SOC range.

[0016] Optionally, the constraint conditions for determining the relationship between the thermal response characteristic parameters and the electrochemical parameters include:

[0017] Based on the variation law of interface impedance and thermal stress under the aforementioned thermal response characteristic parameters, constraint conditions for triggering interface coupling failure risk of battery performance are constructed.

[0018] Optionally, constructing the target optimization model based on the first mapping relationship and the constraints includes:

[0019] An objective function is constructed based on the target heating rate and the target temperature uniformity. Under the constraints, the objective optimization model is constructed, wherein the first mapping relationship is used to generate the heating rate and temperature uniformity.

[0020] Optionally, constructing the target optimization model based on the first mapping relationship and the constraints further includes:

[0021] A second mapping relationship is established between heating excitation parameters and the thermal response characteristic parameters of the battery, wherein the heating excitation parameters include excitation parameters applied by a heating film and / or a liquid thermal means;

[0022] An objective function is constructed based on the target heating rate and the target temperature uniformity. Under the constraints, the objective optimization model is constructed, wherein the first mapping relationship and the second mapping relationship are used to generate the heating rate and temperature uniformity.

[0023] Optionally, the constraint also includes the maximum charge / discharge current of the battery.

[0024] In a second aspect, the present invention provides a battery pulse heating device, comprising:

[0025] The mapping module is used to establish the first mapping relationship between the pulse excitation parameters and the thermal response characteristic parameters of the battery;

[0026] The constraint module is used to determine the constraint conditions between the thermal response characteristic parameters and the electrochemical parameters;

[0027] The model building module is used to build a target optimization model based on the first mapping relationship and the constraints, wherein the target optimization model is used to determine the actual pulse excitation parameters based on the obtained SOC, battery temperature and battery internal resistance.

[0028] Thirdly, the present invention provides a vehicle including a memory and a processor;

[0029] The memory is used to store computer programs;

[0030] The processor is configured to implement the battery pulse heating method as described in the first aspect when executing the computer program.

[0031] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the battery pulse heating method as described in the first aspect.

[0032] The beneficial effects of the battery pulse heating method of the present invention are:

[0033] By establishing a primary mapping relationship between pulse excitation parameters and battery thermal response characteristics, a basis for matching pulse excitation with the battery's dynamic thermal response is provided, thereby improving the battery's heating efficiency in low-temperature environments. Based on this, constraints between thermal response characteristics and electrochemical parameters are further constructed to avoid battery damage caused by improper parameter settings. By establishing a target optimization model incorporating the above mapping relationship and constraints, combined with real-time collected battery state of charge, temperature, and internal resistance data, the optimal pulse frequency and amplitude under the current operating conditions can be dynamically calculated and output. This scheme can adapt to different SOC levels, temperature gradients, and internal resistance changes, effectively optimizing heat generation efficiency. Simultaneously, by establishing constraints, the risks of local overheating and interface lithium deposition are suppressed, significantly improving battery safety and lifespan. Attached Figure Description

[0034] Figure 1 This is a schematic flowchart of the battery pulse heating method according to an embodiment of the present invention;

[0035] Figure 2 This is an example diagram of the first mapping relationship of the battery pulse heating method according to an embodiment of the present invention;

[0036] Figure 3 This is an example diagram of a vehicle according to an embodiment of the present invention. Detailed Implementation

[0037] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0038] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0039] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0040] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0041] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0042] This embodiment provides a battery pulse heating method, apparatus, vehicle, and storage medium.

[0043] like Figure 1 As shown, an embodiment of the present invention provides a battery pulse heating method, comprising:

[0044] Step S100: Establish the first mapping relationship between the pulse excitation parameters and the thermal response characteristic parameters of the battery.

[0045] In the traditional low-temperature heating process of power batteries, the lack of a quantitative correlation model between pulse excitation parameters and the dynamic thermal response characteristics of the battery leads to the inability to accurately match the pulse frequency and amplitude settings with the current electrochemical state of the battery. For example, in the low-temperature cold start scenario of electric vehicles, when the battery's state of charge is in the range of 20%-30% and the cell temperature is below -10℃, the traditional pulse heating system uses a preset fixed frequency (such as 1Hz) and constant current amplitude (such as 5C rate) for excitation. The actual heat generation rate cannot meet the demand for rapid heating. At the same time, the impedance fluctuation of the interface caused by the high-frequency pulse exceeds the material's tolerance threshold. Under multiple low-temperature cycle conditions, the maximum usable capacity of the battery will decay at a rate exceeding the design expectation.

[0046] In this embodiment, by studying the influence of different pulse frequencies and amplitudes on the electrochemical interface impedance, a parameter mapping system that can reflect the real-time state of the battery is established. Further analysis reveals that simply relying on preset parameters cannot adapt to SOC fluctuations and temperature gradient changes; therefore, an optimization model incorporating multivariate constraints must be constructed to achieve dynamic parameter matching.

[0047] The pulse excitation parameters represent the combination of frequency and amplitude of the electrical pulses used to control the internal heat generation process of the battery. Specifically, they can be determined by experimental calibration or electrochemical-thermal coupling model simulation. Adjusting these parameters directly affects the efficiency of internal heat generation and temperature distribution of the battery.

[0048] Thermal response characteristic parameters represent indicators that reflect the rate and distribution of temperature change after the battery is heated. Specifically, they include the heat generation rate and temperature gradient distribution. These parameters can be measured by a network of temperature sensors or calculated based on the thermal properties of the material. They are used to evaluate the uniformity and efficiency of the heating process.

[0049] The first mapping relationship represents the quantitative correlation between pulse frequency, amplitude, heat generation efficiency, and temperature distribution. By analyzing the thermal response data under different pulse parameter combinations, a prediction model is formed, providing a data basis for parameter optimization.

[0050] First, a preliminary mapping relationship is established between pulse excitation parameters and battery thermal response characteristic parameters. Specifically, a quantitative relationship model is established by analyzing the influence of pulse frequency and amplitude on the internal heat generation efficiency and temperature distribution of the battery. The pulse excitation parameters include pulse frequency and amplitude, while the thermal response characteristic parameters include heat generation efficiency and temperature distribution.

[0051] Step S200: Determine the constraints between the thermal response characteristic parameters and the electrochemical parameters.

[0052] Constraints represent the permissible boundaries of changes in battery electrochemical parameters during thermal excitation. Specifically, they include the interfacial impedance growth threshold and the thermal stress limit, which can be determined through cyclic aging experiments or material mechanics tests. They are used to limit the range of pulse parameters to avoid battery performance degradation.

[0053] Determine the constraints between thermal response parameters and electrochemical parameters. This can limit the changes in interfacial impedance and thermal stress, avoiding the risk of battery damage due to thermal runaway or material failure.

[0054] Step S300: Construct a target optimization model based on the first mapping relationship and the constraints, wherein the target optimization model is used to determine the actual pulse excitation parameters based on the obtained SOC, battery temperature and battery internal resistance.

[0055] The target optimization model represents a multivariate decision system with heating rate and temperature uniformity as optimization objectives. It is constructed using dynamic programming or genetic algorithms. The optimal combination of pulse parameters is generated by real-time input of battery state of charge, temperature and internal resistance data to achieve a dynamic balance between heating efficiency and safety boundary.

[0056] A target optimization model is constructed based on the first mapping relationship and constraints. This model uses SOC, battery temperature, and battery internal resistance as input variables, dynamically matching the optimal pulse parameter combination while satisfying safety constraints. Based on the obtained SOC, battery temperature, and battery internal resistance, the actual pulse excitation parameters are determined through the target optimization model. This enables the synergistic optimization of heating efficiency and battery life under low-temperature conditions.

[0057] The core innovation of this application lies in establishing a quantitative mapping relationship between pulse parameters and thermal response, and constructing a dynamic optimization model by combining the safety constraint boundary of electrochemical parameters. This enables the pulse excitation parameters to be adaptively adjusted according to the real-time state of the battery, thereby simultaneously improving heating efficiency and controlling battery damage risk in low-temperature environments.

[0058] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0059] A first mapping relationship is established between pulse excitation parameters and battery thermal response characteristic parameters. Battery thermal characteristic tests are conducted, collecting internal heat generation data under different pulse frequencies and amplitudes. Thermal imaging technology is used to acquire battery surface temperature distribution information. Based on the test data, a machine learning algorithm is used to establish a mapping model between pulse parameters and thermal response characteristics. Constraints between thermal response characteristic parameters and electrochemical parameters are determined. Electrochemical impedance spectroscopy is performed to analyze interfacial impedance changes under different thermal response conditions. Finite element analysis is used to simulate the internal thermal stress distribution of the battery. Safety thresholds for interfacial impedance and thermal stress are determined based on material properties. A target optimization model is constructed, setting heating rate and temperature uniformity as optimization objectives. The first mapping relationship serves as the data foundation for the target optimization model, with constraints acting as boundary conditions. A genetic algorithm is used to solve for the optimal pulse parameter combination, obtaining the target optimization model. Battery SOC and temperature are acquired in real time, and the target optimization model calculates the optimal pulse frequency and amplitude under the current state. The optimized pulse parameters are then applied to battery heating control.

[0060] In this embodiment, a first mapping relationship is established between pulse excitation parameters and battery thermal response characteristic parameters to provide a basis for matching pulse excitation with the battery's dynamic thermal response, thereby improving the battery's heating efficiency in low-temperature environments. Based on this, constraints are further established between thermal response characteristic parameters and electrochemical parameters to avoid battery damage caused by improper parameter settings. By establishing a target optimization model that includes the above mapping relationship and constraints, combined with real-time collected battery state of charge, temperature, and internal resistance data, the optimal pulse frequency and amplitude under the current operating conditions can be dynamically calculated and output. This scheme can adapt to different SOC levels, temperature gradients, and internal resistance changes, effectively optimizing heat generation efficiency. Simultaneously, by controlling interface stress accumulation, it suppresses the risk of local overheating and interface lithium deposition, significantly improving battery safety and lifespan.

[0061] Optionally, establishing the first mapping relationship between the pulse excitation parameters and the thermal response characteristic parameters of the battery includes:

[0062] Based on the influence of pulse frequency and amplitude on the heat generation efficiency and temperature distribution inside the battery cell, the first mapping relationship is established, wherein the pulse excitation parameters include the pulse frequency and the amplitude, and the thermal response characteristic parameters include the heat generation efficiency and the temperature distribution.

[0063] In one embodiment, such as Figure 2As shown, the pulse frequency is configured to control the period of current change, and its adjustment range can be 1Hz-1200Hz. For example, a 10Hz pulse frequency is used in low-temperature environments to reduce the proportion of heat generated by polarization reactions. The pulse amplitude is configured to determine the current intensity, and its setting range can be 0-600A. The internal resistance range is divided into multiple segments from 0.1mΩ to 5mΩ, each segment corresponding to a different initial temperature range. For example, when the internal resistance is 2mΩ, the initial temperature range is limited to -20℃ to -15℃. The initial SOC range is divided into multiple intervals, each interval corresponding to a different combination of pulse parameters.

[0064] By experimentally measuring heat generation efficiency data under different pulse frequencies and amplitudes, a quantitative relationship curve between the frequency-amplitude two-dimensional parameter space and heat generation efficiency was established. For example, with SOH=95%, SOC=50%, and an initial battery temperature ranging from -20℃ to -5℃, the temperature rise rate was 0.053℃ / min at a frequency of 50Hz and an amplitude of 80A. This allows for a precise establishment of the relationship between pulse excitation parameters and battery thermal response characteristics, providing a reliable data foundation for subsequent optimization of the pulse heating process. By considering the influence of pulse frequency and amplitude on heat generation efficiency and temperature distribution, the internal heat generation process and temperature uniformity of the battery can be more accurately controlled. This allows for better adaptation to the battery's heating requirements under different operating conditions, avoiding problems such as localized overheating or insufficient overall heating efficiency caused by improper parameter settings.

[0065] Optionally, establishing the first mapping relationship based on the influence of pulse frequency and amplitude on the heat generation efficiency and temperature distribution inside the battery cell includes:

[0066] The range of variation of the cell's internal resistance is divided to obtain internal resistance sub-ranges;

[0067] The corresponding initial temperature range and initial SOC range of the battery cell are determined based on the internal resistance range.

[0068] The first mapping relationship is established under the constraints of the initial temperature range of the battery cell and the initial SOC range.

[0069] Since internal resistance determines the heating rate, dividing the range of internal resistance variation within the cell into multiple sub-ranges allows for differentiated treatment of battery characteristics under different internal resistance states. For example, internal resistance sub-ranges can be divided according to gradients, with each sub-range corresponding to a different initial temperature range and SOC range. Constraints are then applied based on the initial temperature range and SOC range corresponding to each sub-range. Under these initial constraints, a mapping relationship is established between pulse frequency, amplitude, heat generation efficiency, and temperature distribution for each internal resistance sub-range. The heat generation efficiency can be expressed using the Joule heating formula Q=I. 2RΔt is calculated, and the temperature distribution is obtained through finite element simulation or experimental calibration, where Q represents heat, I represents current, R represents battery internal resistance, and Δt represents time period. Therefore, by dividing the internal resistance sub-intervals and associating them with initial conditions, the established mapping relationship can dynamically adapt to changes in battery internal resistance, avoiding the insufficient parameter generalization ability caused by single-interval modeling, thereby improving the adaptability of pulse heating parameters and heating efficiency.

[0070] This approach enables refined modeling of battery dynamic characteristics, improves the accuracy and relevance of the correlation between pulse excitation parameters and thermal response characteristic parameters, and enhances the adaptability of the pulse heating method in complex battery operating environments. By considering the coupling relationship between internal resistance, temperature, and SOC, the efficiency and safety of pulse heating are improved, effectively avoiding the problem of insufficient adaptability of pulse excitation parameters that may be caused by establishing a mapping relationship based on a single internal resistance range.

[0071] Optionally, the constraint conditions for determining the relationship between the thermal response characteristic parameters and the electrochemical parameters include:

[0072] Based on the variation law of interface impedance and thermal stress under the aforementioned thermal response characteristic parameters, constraint conditions for triggering interface coupling failure risk of battery performance are constructed.

[0073] The variation law of interface impedance can be obtained by impedance spectrum analysis or equivalent circuit model to monitor the state of the electrode-electrolyte interface in real time, and corresponding pulse amplitude constraints can be constructed accordingly. For example, when the interface impedance changes by more than 20% with temperature gradient in the range of pulse frequency of 1-10Hz, the constraint condition is triggered to limit the pulse amplitude. The variation law of thermal stress can be determined by obtaining the deformation data of battery material through finite element simulation or strain sensor, and corresponding pulse frequency constraints can be constructed accordingly. For example, when the local thermal stress exceeds 80% of the material yield strength, the corresponding pulse frequency adjustment threshold is generated.

[0074] By integrating interface impedance and thermal stress data into the constraint generation module, the allowable range of pulse frequency and amplitude in the first mapping relationship can be dynamically corrected. For example, different interface impedances correspond to different pulse frequency correction values, and different thermal stress change patterns correspond to different pulse amplitude correction values. The initial allowable range of frequency and initial allowable range of amplitude are corrected by the pulse frequency correction value and the pulse amplitude correction value to obtain the final allowable range. At the same time, combined with the cell initial temperature range and initial SOC range established in the aforementioned scheme, a multi-dimensional parameter collaborative optimization mechanism is formed.

[0075] During pulse heating, the variation of interfacial impedance can be monitored by observing the fluctuations in charge transfer resistance under different temperature gradients, thereby identifying regions with abnormal lithium-ion concentration on the electrode surface. For example, when the temperature distribution difference causes a significant drop in local charge transfer resistance, constraints can be applied to limit the pulse frequency and amplitude in that region, thus suppressing lithium metal deposition in that region. The variation of thermal stress can be analyzed by examining the strain difference between the cathode material and the current collector to predict the risk of interfacial delamination. For example, when the temperature rise rate exceeds 5°C / min, constraints can be used to limit the pulse frequency increase to avoid stress concentration.

[0076] To prevent battery performance degradation and structural damage caused by uneven temperature distribution or sudden thermal stress, pulse excitation parameters can be dynamically adjusted by incorporating the variation patterns of interface impedance and thermal stress into the constraints, ensuring the interface stability and structural integrity of the battery during heating. This not only improves the safety of pulse heating but also extends battery life. Furthermore, by considering the dynamic influence of thermal response characteristics on the battery's internal interface impedance and thermal stress, the heating process can be controlled more precisely, improving heating efficiency and uniformity.

[0077] Optionally, constructing the target optimization model based on the first mapping relationship and the constraints includes:

[0078] An objective function is constructed based on the target heating rate and the target temperature uniformity. Under the constraints, the objective optimization model is constructed, wherein the first mapping relationship is used to generate the heating rate and temperature uniformity.

[0079] In one embodiment, the objective function is constructed including a heating rate target and a temperature uniformity target, with weighting coefficients dynamically adjusted according to battery type or operating conditions. Constraints also include an interface impedance change rate threshold and a thermal stress safety range. At low temperatures, excessively rapid heating can easily lead to lithium metal deposition on the negative electrode surface, puncturing the separator and causing an internal short circuit, resulting in thermal runaway. Rapid heating may also cause uneven temperature distribution within the battery, exacerbating material thermal expansion mismatch and causing structural damage. The heating rate represents the rate of temperature rise in a specific region of the battery; temperature uniformity represents the uniformity of temperature rise in different sub-regions during the heating process. Based on the first mapping relationship, after determining the temperature and SOC, the range of the heating rate can be further determined. By dividing the battery into regions, the standard deviation of the temperature in each region is calculated to quantify the temperature difference, which is used to represent temperature uniformity. The temperature uniformity value range and the heating rate value range are determined within the interface impedance change rate threshold and the thermal stress safety range. Within the heating rate value range and the temperature uniformity value range, appropriate target heating rate and target temperature uniformity are selected as part of the objective function.

[0080] Specifically, the target optimization model correlates pulse parameters with thermal response parameters through a first mapping relationship. The pulse frequency and pulse amplitude can be mapped to a quadratic function of heat generation efficiency, while the temperature distribution is modeled using a spatial gradient model established through the heat transfer equation. During model solving, the heating rate objective drives the pulse parameters towards higher frequency and higher current, while the temperature uniformity objective reduces the local temperature rise rate by adjusting the pulse interval. Under the premise of meeting electrochemical parameter constraints, the pulse excitation parameters can be dynamically adjusted to achieve a preset optimal balance between heating rate and temperature uniformity. This effectively solves the problem that relying solely on the basic mapping relationship may lead to an inability to balance heating rate and temperature uniformity, improving the coordination between heating efficiency and the internal temperature distribution of the battery.

[0081] For example, an objective function is constructed based on the target heating rate and target temperature uniformity, and an objective optimization model is built under constraints. The first mapping relationship is used to generate the heating rate and temperature uniformity. Specifically, the preset heating rate can be set to 4-6°C per minute, and the preset temperature uniformity can be set to a maximum internal temperature difference of no more than 2°C. Based on these preset values, an objective function containing heating rate and temperature uniformity terms is constructed. For example, the objective function can be expressed as:

[0082] f = w1(actual heating rate - preset heating rate) 2 + w2(Actual maximum temperature difference - Preset maximum temperature difference) 2 ,

[0083] Here, f represents the objective function, and w1 and w2 represent weighting coefficients. Under constraints, heating rate and temperature uniformity data under different pulse excitation parameters are generated using the first mapping relationship. The pulse frequency and amplitude are iteratively adjusted using optimization algorithms such as gradient descent to minimize the objective function value, thereby obtaining the optimal actual pulse excitation parameters.

[0084] Optionally, constructing the target optimization model based on the first mapping relationship and the constraints further includes:

[0085] A second mapping relationship is established between heating excitation parameters and the thermal response characteristic parameters of the battery, wherein the heating excitation parameters include excitation parameters applied by a heating film and / or a liquid thermal means;

[0086] An objective function is constructed based on the target heating rate and the target temperature uniformity. Under the constraints, the objective optimization model is constructed, wherein the first mapping relationship and the second mapping relationship are used to generate the heating rate and temperature uniformity.

[0087] The second mapping relationship includes the mapping between the excitation parameters applied by the heating method and the thermal response characteristic parameters. For example, when the heating method is heating film heating, the second mapping relationship includes the mapping between the heating excitation parameters of the heating film and the thermal response characteristic parameters of the battery; when the heating method is liquid heating, the second mapping relationship includes the mapping between the liquid heating excitation parameters and the thermal response characteristic parameters of the battery. The heating film excitation parameters include the current applied to the heating film, and the liquid heating excitation parameters include the heating liquid temperature and flow rate. When both heating methods are used simultaneously, the second mapping relationship correlates the heating film power density with the liquid heating heat exchange coefficient, forming a multi-dimensional parameter space. During the construction of the objective function, the heat generated by the pulse excitation and the heat conduction by the liquid heating are weighted to achieve multi-heat source coordinated control.

[0088] In low-temperature environments, by acquiring the battery's SOC and temperature, a first mapping relationship corresponding to the current battery SOC and temperature is invoked. Since the first mapping relationship characterizes the mapping relationship between amplitude, frequency, and temperature rise rate, a suitable initial pulse frequency and amplitude can be determined based on heating efficiency requirements or through a target optimization model. Based on the differences in battery surface temperature distribution, a second mapping relationship corresponding to the heating film or liquid thermal system is activated. For example, when the temperature at the edge of the cell is detected to be lower than that of the central region, the liquid thermal system is activated to improve convective heat transfer efficiency, and the heating liquid flow rate and pulse frequency are matched according to the second mapping relationship. The target optimization model dynamically adjusts the pulse excitation and external heating parameters through iterative calculations, ensuring that the heat generation rate reaches a preset threshold while improving temperature uniformity. Based on pulse heating, the thermal radiation characteristics of the heating film and the thermal convection characteristics of the liquid thermal system are spatially complementary, and the Joule heating from the pulse excitation alternates with the external heating in time, thereby overcoming the thermal conduction efficiency limitations of a single heat source and achieving an overall improvement in heating efficiency.

[0089] Optionally, the constraint also includes the maximum charge / discharge current of the battery.

[0090] The constraint on the maximum charge / discharge current can be set based on the battery's physical characteristics or historical operating data. This constraint, combined with preset heating rate and temperature uniformity objective functions, ensures that it does not exceed the battery's maximum withstand threshold. This effectively avoids the risk of current exceeding limits and ensures battery safety during the heating process.

[0091] An embodiment of the present invention provides a battery pulse heating device, comprising:

[0092] The mapping module is used to establish the first mapping relationship between the pulse excitation parameters and the thermal response characteristic parameters of the battery;

[0093] The constraint module is used to determine the constraint conditions between the thermal response characteristic parameters and the electrochemical parameters;

[0094] The model building module is used to build a target optimization model based on the first mapping relationship and the constraints, wherein the target optimization model is used to determine the actual pulse excitation parameters based on the obtained SOC, battery temperature and battery internal resistance.

[0095] like Figure 3 As shown, an embodiment of the present invention provides a vehicle 300, including a memory 310 and a processor 320; the memory 310 is used to store a computer program; the processor 320 is used to implement the battery pulse heating method as described above when the computer program is executed.

[0096] Alternatively, a vehicle 300 includes a memory 310 and a processor 320 coupled to the memory 310; the memory 310 is configured to store a computer program; the processor 320 is configured to perform the following operations when the computer program is executed:

[0097] Establish the first mapping relationship between the pulse excitation parameters and the thermal response characteristic parameters of the battery;

[0098] Determine the constraints between the thermal response characteristic parameters and the electrochemical parameters;

[0099] A target optimization model is constructed based on the first mapping relationship and the constraints, wherein the target optimization model is used to determine the actual pulse excitation parameters based on the obtained SOC, battery temperature and battery internal resistance.

[0100] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the battery pulse heating method described above.

[0101] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations:

[0102] Establish the first mapping relationship between the pulse excitation parameters and the thermal response characteristic parameters of the battery;

[0103] Determine the constraints between the thermal response characteristic parameters and the electrochemical parameters;

[0104] A target optimization model is constructed based on the first mapping relationship and the constraints, wherein the target optimization model is used to determine the actual pulse excitation parameters based on the obtained SOC, battery temperature and battery internal resistance.

[0105] Vehicle 300, which can serve as a server or client of the present invention, is now described as an example of a hardware device incorporating various aspects of the present invention. Vehicle 300 includes various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Vehicle 300 also includes various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0106] Vehicle 300 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or a computer program loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0107] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0108] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A battery pulse heating method, characterized in that, include: Establishing a first mapping relationship between pulse excitation parameters and battery thermal response characteristic parameters includes: establishing the first mapping relationship based on the influence of pulse frequency and amplitude on the heat generation efficiency and temperature distribution inside the battery cell, wherein the pulse excitation parameters include the pulse frequency and the amplitude, and the thermal response characteristic parameters include the heat generation efficiency and the temperature distribution; Determining the constraints between the thermal response characteristic parameters and electrochemical parameters includes: constructing the constraints that trigger the risk of interface coupling failure of battery performance based on the variation law of interfacial impedance and thermal stress under the thermal response characteristic parameters; Constructing a target optimization model based on the first mapping relationship and the constraints includes: establishing a second mapping relationship between heating excitation parameters and battery thermal response characteristic parameters, wherein the heating excitation parameters include excitation parameters applied by a heating film and / or liquid thermal means; An objective function is constructed based on the target heating rate and the target temperature uniformity. Under the constraints, the objective optimization model is constructed. The first mapping relationship and the second mapping relationship are used to generate the heating rate and temperature uniformity. The objective optimization model is used to determine the actual pulse excitation parameters based on the obtained SOC, battery temperature and battery internal resistance.

2. The battery pulse heating method according to claim 1, characterized in that, The establishment of the first mapping relationship based on the influence of pulse frequency and amplitude on the heat generation efficiency and temperature distribution inside the battery cell includes: The range of variation of the cell's internal resistance is divided to obtain internal resistance sub-ranges; The corresponding initial temperature range and initial SOC range of the battery cell are determined based on the internal resistance range. The first mapping relationship is established under the constraints of the initial temperature range of the battery cell and the initial SOC range.

3. The battery pulse heating method according to claim 1, characterized in that, The constraints also include the maximum charge / discharge current of the battery.

4. A battery pulse heating device, characterized in that, include: A mapping module is used to establish a first mapping relationship between pulse excitation parameters and thermal response characteristic parameters of the battery, including: establishing the first mapping relationship based on the influence of pulse frequency and amplitude on the heat generation efficiency and temperature distribution inside the battery cell, wherein the pulse excitation parameters include the pulse frequency and the amplitude, and the thermal response characteristic parameters include the heat generation efficiency and the temperature distribution; The constraint module is used to determine the constraint conditions between the thermal response characteristic parameters and the electrochemical parameters, including: constructing the constraint conditions that trigger the interface coupling failure risk of the battery performance based on the variation law of the interface impedance and thermal stress under the thermal response characteristic parameters. The model building module is used to build a target optimization model based on the first mapping relationship and the constraints, including: establishing a second mapping relationship between heating excitation parameters and battery thermal response characteristic parameters, wherein the heating excitation parameters include excitation parameters applied by a heating film and / or liquid thermal means; constructing an objective function based on a target heating rate and a target temperature uniformity, and constructing the target optimization model under the constraints, wherein the first mapping relationship and the second mapping relationship are used to generate the heating rate and temperature uniformity, and wherein the target optimization model is used to determine the actual pulse excitation parameters based on the obtained SOC, battery temperature and battery internal resistance.

5. A vehicle, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the battery pulse heating method as described in any one of claims 1-3 when executing the computer program.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the battery pulse heating method as described in any one of claims 1-3.

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