Lithium battery equipment low-temperature environment self-adaptive PWM heating control method and equipment and storage medium

Abstract

The invention relates to battery thermal management control, and discloses a lithium battery equipment low-temperature environment adaptive PWM heating control method comprising the following steps: dividing a battery pack into N thermal management areas; acquiring a voltage value, a current value, a surface temperature, an environment temperature, a charge state, a health state and current working mode information of each area of the battery pack; based on the voltage value, the current value and the surface temperature of each area of the battery pack and a pre-stored battery thermal resistance parameter, calculating the internal temperature of the battery pack in each area, and comparing the internal temperature with a target temperature to obtain a temperature difference value; and on the basis of the temperature difference value, the charge state, the health state and the current working mode information of each region of the battery pack, generating a heating enabling value corresponding to each region, so that when the heating enabling value of a certain region is greater than or equal to a preset heating starting threshold value, a heating loop in the corresponding region is activated. The problems of battery performance degradation and safety risk of equipment in a low-temperature environment are solved, and energy consumption and protection effects are collaboratively optimized.

Description

Technical Field

[0001] This invention relates to the field of battery thermal management technology, and in particular to a method, device and storage medium for adaptive PWM heating control of lithium battery devices in low-temperature environments. Background Technology

[0002] With the widespread application of electric vehicles, large-scale energy storage, drones, and various portable electronic devices, the reliable operation of battery packs in low-temperature environments such as high latitudes or high altitudes is becoming increasingly critical. However, commonly used chemical systems such as lithium-ion batteries are prone to safety hazards such as a surge in internal resistance, a sharp drop in usable capacity, deterioration in charging ability, and even lithium plating below 0°C, severely restricting device performance and range. Existing technologies mostly employ fixed duty cycle PWM heating or linear PID control based on surface temperature, which struggles to balance heating efficiency and energy consumption. Furthermore, they generally ignore the impact of uneven internal heat distribution, individual cell aging differences, and dynamic operating conditions (such as vehicle acceleration, energy storage charging and discharging, and high-load equipment operation) on thermal management requirements, easily leading to localized overheating or insufficient heating, posing a risk of thermal runaway. Therefore, there is an urgent need for an adaptive PWM heating control method that integrates battery internal state, environmental parameters, and operating modes to achieve precise, safe, and efficient low-temperature thermal management, comprehensively improving the operational reliability and battery life of various lithium-ion battery-containing devices in harsh environments. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, and storage medium for adaptive PWM heating control in low-temperature environments for lithium battery devices, in order to solve the problems mentioned in the background art.

[0004] The purpose of this application is to provide a method, device and storage medium for adaptive PWM heating control of lithium battery devices in low-temperature environments, so as to solve the problems of battery performance degradation and safety risks in low-temperature environments, achieve synergistic optimization of energy consumption and protection effect, and improve the stable operation capability of battery packs in low-temperature environments.

[0005] This application provides an adaptive PWM heating control method for lithium battery devices in low-temperature environments, the technical solution of which is as follows:

[0006] The low-temperature adaptive PWM heating control method for lithium battery equipment includes:

[0007] The battery pack is divided into N thermal management zones, and each zone is equipped with an independent and controllable heating circuit and temperature sensor.

[0008] Acquire information on voltage, current, surface temperature, ambient temperature, state of charge, health status, and current operating mode for each region of the battery pack;

[0009] The voltage value, current value, surface temperature and pre-stored battery thermal resistance parameter of each region of the battery pack are input into a preset electro-thermal coupling model to calculate the internal temperature of each region of the battery pack, and a temperature difference value is obtained by comparing the internal temperature with a target temperature.

[0010] Based on the temperature difference value, state of charge, state of health and current working mode information of each region of the battery pack, the preset heating trigger formula is input to generate a heating enable value corresponding to each region, so that when the heating enable value of a certain region is greater than or equal to a preset heating start threshold, the heating circuit under the corresponding region is activated.

[0011] Further, the application also proposes that the working mode includes one or more of standby, normal operation, high load operation, emergency state, charging state and storage state.

[0012] Further, the application also proposes that the step of inputting the voltage value, current value, surface temperature and pre-stored battery thermal resistance parameter of each region of the battery pack into a preset electro-thermal coupling model to calculate the internal temperature of each region of the battery pack, and obtaining a temperature difference value by comparing the internal temperature with a target temperature includes:

[0013] Based on the current time, the heat generation power is calculated based on the current value and voltage value of the battery pack in each region.

[0014] The heat generation power, surface temperature and pre-stored battery thermal resistance parameter are input into a preset electro-thermal coupling model to generate an internal temperature, and the internal temperature is compared with a preset target working temperature to obtain a temperature difference value.

[0015] Further, the application also proposes that the step of inputting the temperature difference value, state of charge, state of health and current working mode information of each region of the battery pack into a preset heating trigger formula to generate a heating enable value corresponding to each region, so that when the heating enable value of a certain region is greater than or equal to a preset heating start threshold, the heating circuit under the corresponding region is activated includes:

[0016] The temperature difference value, state of charge and state of health of the i-th region of the battery pack are input into the corresponding preset heating trigger formula to obtain a standardized heat demand factor, an electric quantity risk factor and an aging compensation factor;

[0017] According to the current working mode, the weight coefficients corresponding to the heat demand factor, the electric quantity risk factor and the aging compensation factor are obtained from a pre-stored weight table, so that the heating enable value of the i-th region is generated by weighting and summing the standardized heat demand factor, the electric quantity risk factor and the aging compensation factor;

[0018] If the heating enable value is greater than or equal to the preset heating start threshold, the heating circuit under the i-th region is activated.

[0019] Further, the application also proposes that, based on the temperature difference value, state of charge, state of health and current working mode information of each area of the battery pack, the input to the preset heating trigger formula generates the heating enable value corresponding to each area, and after the step of activating the heating circuit under the corresponding area when the heating enable value of a certain area is greater than or equal to the preset heating start threshold value:

[0020] Based on the internal temperature and the state of charge, an initial duty cycle is generated according to a preset duty cycle lookup table;

[0021] According to the deviation of the rate of change of the internal temperature and the preset target temperature rise rate, a duty cycle correction amount is determined;

[0022] The initial duty cycle and the duty cycle correction amount are superimposed and then subjected to amplitude limiting processing to generate a working PWM duty cycle to control the working power of the activated heating circuit.

[0023] Further, the application also proposes that, after the step of generating the working PWM duty cycle after superimposing the initial duty cycle and the duty cycle correction amount and then subjecting to amplitude limiting processing, the step further includes:

[0024] Based on the current working mode and / or the state of charge, a maximum working duty cycle upper limit is set;

[0025] The amplitude limiting processing limits the superimposed duty cycle between 0 and the maximum working duty cycle.

[0026] Further, the application also proposes a self-adaptive PWM heating control lithium battery device, the heating control device comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the above-mentioned lithium battery device low-temperature environment self-adaptive PWM heating control method.

[0027] Further, the application also proposes a storage medium, the storage medium being a computer readable storage medium, and the storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the above-mentioned lithium battery device low-temperature environment self-adaptive PWM heating control method.

[0028] As can be seen from the above, the application provides a lithium battery device low-temperature environment self-adaptive PWM heating control method, device and storage medium, by dividing the thermal management area, obtaining multi-dimensional parameters, calculating the internal temperature and generating self-adaptive heating enable value based on the temperature difference value and state information, the problem that the prior art cannot real-time perceive the internal thermal state of the battery and integrate the working mode and other variables is solved, which has the advantages of solving the battery performance degradation and equipment safety risk problem in low-temperature environment, realizing the collaborative optimization of energy consumption and protection effect, and improving the stable operation ability of the battery pack in low-temperature environment. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The first embodiment flowchart is provided for the low-temperature environment adaptive PWM heating control method of the lithium battery device of the present application.

[0030] Figure 2 The sub-flowchart of step S300 is provided for the low-temperature environment adaptive PWM heating control method of the lithium battery device of the present application.

[0031] Figure 3 The sub-flowchart of step S400 is provided for the low-temperature environment adaptive PWM heating control method of the lithium battery device of the present application.

[0032] Figure 4 The second embodiment flowchart is provided for the low-temperature environment adaptive PWM heating control method of the lithium battery device of the present application.

[0033] Figure 5 The sub-flowchart of step S700 of the second embodiment is provided for the low-temperature environment adaptive PWM heating control method of the lithium battery device of the present application.

[0034] Figure 6 The device structure involved in the adaptive PWM heating control lithium battery device in the embodiments of the present application is shown in the schematic diagram. DETAILED DESCRIPTION

[0035] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The technical solutions of the present application will be further described below in conjunction with the drawings of the embodiments of the present application. The present application is not limited to the following specific embodiments.

[0036] It should be understood that the same or similar reference numerals in the drawings of the embodiments correspond to the same or similar components. In the description of the present application, it should be understood that the orientations or positional relationships indicated by the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and cannot be understood as a limitation of the present patent, for those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0037] When the conventional existing battery pack operates in a low-temperature environment, lithium ion batteries are prone to have increased internal resistance, sudden capacity drop, deteriorated charging capacity, lithium precipitation and other hidden dangers, which restricts the performance and endurance of the battery. The existing technology mostly uses fixed duty ratio PWM heating or linear PID control based on surface temperature, which is difficult to balance heating efficiency and energy consumption, and generally ignores the influence of uneven internal heat distribution, single cell aging difference and dynamic working condition on thermal management demand, which is easy to cause local overheating or insufficient heating, and has the risk of thermal runaway.

[0038] Reference Figures 1 to 5 To this end, the present application proposes a low-temperature environment adaptive PWM heating control method for lithium battery equipment. This method divides the battery pack into N thermal management areas and configures a heating circuit with independent control capability and a temperature sensor for each area, achieving fine thermal management. This method can obtain the voltage value, current value, surface temperature, ambient temperature, state of charge, state of health and current working mode information of each area of the battery pack, and calculate the internal temperature using an electro-thermal coupling model. Then, based on the temperature difference value, state of charge, state of health and working mode information, a heating enable value is generated through a preset heating trigger formula to adaptively activate the heating circuit of the corresponding area, thereby effectively solving the problem of difficult balance between heating efficiency and energy consumption in the prior art and non-fine thermal management.

[0039] For ease of understanding, some key terms in the present embodiment are explained as follows:

[0040] The division of the battery pack into N thermal management areas means that the entire battery pack carried by the equipment (such as electric vehicles, energy storage systems, portable electronic devices, drones, etc.) is logically or physically divided into multiple independent sub-areas according to its physical structure, thermal distribution characteristics or management needs. This division aims to achieve fine management of the temperature at different positions inside the battery pack to cope with local hot spots or uneven temperature conditions.

[0041] The heating circuit with independent control capability refers to the heating unit and its control circuit separately configured for each thermal management area of the battery pack. Each heating circuit, such as a PWM drive module, can independently turn on, turn off or adjust the heating power, thereby achieving accurate temperature control of the respective area and avoiding local overheating or insufficient heating caused by uniform heating of the entire battery pack.

[0042] The temperature sensor is a device used to monitor the temperature of each thermal management area of the battery pack in real time. These sensors are usually arranged on the surface or inside the battery pack to obtain accurate temperature data for subsequent temperature calculation and heating control.

[0043] Electro-thermal coupling model refers to a mathematical or physical model that describes the conversion relationship between electrical energy and thermal energy during the charging and discharging process of the battery. This model can calculate the temperature distribution or average internal temperature inside the battery based on its electrical and thermal parameters.

[0044] Internal temperature refers to the average actual temperature of the electrochemical reaction area (such as the positive and negative electrodes, the separator, and the electrolyte) of multiple battery cells in a certain region of the battery pack. Compared with the surface temperature, the internal temperature better reflects the true thermal state of the battery, and is of great significance for evaluating the performance, safety, and life of the battery.

[0045] Target temperature refers to a specific temperature value within the ideal working temperature range set for the battery pack. When the internal temperature of the battery deviates from this target temperature, the heating system needs to be started or adjusted to restore it to the vicinity of the target temperature, ensuring the optimal performance and safety of the battery.

[0046] Temperature difference value refers to the difference between the internal temperature of each region of the battery pack and the preset target temperature. This value reflects the deviation of the current battery thermal state from the ideal thermal state, and is an important basis for determining whether heating is needed and the heating intensity.

[0047] State of charge (SOC) refers to the percentage of the remaining capacity of the battery relative to its total capacity. This parameter reflects the available energy of the battery and has an impact on the development of heating strategies, such as limiting heating power to save energy when the battery is low.

[0048] State of health (SOH) refers to the degree of performance degradation of the battery relative to its brand-new state. This parameter reflects the aging degree of the battery, and an aging battery may be more sensitive to temperature changes, so it needs to be considered in heating control.

[0049] Working mode information refers to the current working state, such as standby, regular operation, high-load operation, emergency state, charging state, storage state, etc. Different working modes may have different power requirements and thermal management strategies for lithium battery devices.

[0050] Heating trigger formula refers to a preset mathematical expression or logical rule that considers various state parameters of the battery to determine whether heating needs to be started and generate a heating enable value.

[0051] Heating enable value refers to a quantitative value calculated by the heating trigger formula, indicating the heating demand of the corresponding region. When the heating enable value reaches or exceeds the preset heating start threshold, it indicates that the region needs to start heating.

[0052] The heating start threshold is a preset critical value. When the heating enable value reaches or exceeds the threshold, the system will activate the corresponding heating circuit and start heating the battery pack.

[0053] The embodiment provides a low-temperature environment adaptive PWM heating control method for a lithium battery device, and specifically includes the following steps:

[0054] In step S100, the battery pack is divided into N thermal management regions, and an independently controllable heating circuit and a temperature sensor are configured in each region.

[0055] In step S200, voltage values, current values, surface temperatures, ambient temperatures, state of charge, state of health and current working mode information of each region of the battery pack are obtained.

[0056] In step S300, the voltage values, current values, surface temperatures and pre-stored battery thermal resistance parameters of each region of the battery pack are input into a preset electro-thermal coupling model to calculate the internal temperature of the battery pack in each region, and a temperature difference value is obtained by comparing the internal temperature with a target temperature.

[0057] In step S400, the temperature difference value, state of charge, state of health and current working mode information of each region of the battery pack are input into a preset heating trigger formula to generate a corresponding heating enable value of each region, so that when the heating enable value of a certain region is greater than or equal to a preset heating start threshold, the heating circuit in the corresponding region is activated.

[0058] First, the battery pack is divided into N thermal management regions. For example, the battery pack can be evenly divided into a plurality of sub-regions according to the physical structure of the battery module or the heat dissipation channel inside the battery pack. In each thermal management region, an independently controllable heating circuit and a temperature sensor are configured. The heating circuit can adopt a resistance heating sheet, which is controlled by an independent switching circuit; the temperature sensor can adopt a thermistor, which is used to monitor the temperature of the region in real time.

[0059] Secondly, the voltage value, current value, surface temperature, ambient temperature, state of charge, state of health and current working mode information of each region of the battery pack are acquired. Specifically, the voltage value and current value can be collected in real time by a shunt or a Hall sensor in the battery management system (BMS); the surface temperature is directly measured by a temperature sensor arranged in each region; the ambient temperature is acquired by an external ambient temperature sensor; the state of charge can be estimated by an ampere-hour integral method or a table lookup method based on open-circuit voltage; the state of health can be evaluated by the number of cycles of the battery or a simple measurement of the internal resistance; and the working mode information can be provided by a microprocessor of an electric vehicle, an energy storage system or a portable electronic device, for example, by a simple digital signal indicating that the current mode is standby, regular operation, high-load operation, etc.

[0060] Next, the voltage value, current value, surface temperature and pre-stored thermal resistance parameter of each region of the battery pack are input into a preset electro-thermal coupling model to calculate the internal temperature of the battery pack in each region. The electro-thermal coupling model can be a simplified empirical model, for example, the internal temperature is set to the surface temperature plus a term proportional to the square of the current.

[0061] Finally, based on the temperature difference value, state of charge, state of health and current working mode information of each region of the battery pack, a preset heating trigger formula is input to generate a corresponding heating enable value for each region. The heating trigger formula can be a simple linear combination, and the working mode information can be used as a logical switch for the heating strategy, for example, heating can be suspended or limited in certain specific working modes (such as emergency state). When the heating enable value of a region is greater than or equal to a preset heating start threshold, the heating circuit in the corresponding region is activated. The heating start threshold can be a fixed value, and when the heating enable value reaches the threshold, the power supply of the corresponding heating circuit is directly turned on by the control circuit to start heating at a fixed power.

[0062] The present application realizes adaptive local heating control of the battery pack in a low-temperature environment by dividing the battery pack into regions and combining multi-dimensional battery state parameters and working mode information. This method can effectively avoid the contradiction between heating efficiency and energy consumption in traditional schemes, reduce the risk of local overheating or insufficient heating, and thus improve the operation reliability of the battery pack in a cold environment, prolong the service life of the battery, and ensure the performance and endurance of the battery.

[0063] In some embodiments of the present application, the heating enable value corresponding to each region is generated based on the current working mode information to achieve adaptive heating control of the battery pack. However, in actual applications, the demand for battery performance and heating strategy of lithium battery equipment under different working modes may be significantly different. If the characteristics of these working modes are not clearly distinguished and utilized, the heating control may not be fine enough to fully adapt to various complex task requirements, thereby affecting the performance and life of the battery.

[0064] To this end, the present application further proposes that the working mode comprises one of one or more of standby, regular operation, high-load operation, emergency state, charging state, and storage state.

[0065] Specifically, the working mode refers to different running states of the battery system during task execution. These modes reflect different demands of the system on power output, energy consumption, safety, etc., so the temperature management strategy of the battery should also be focused. The standby mode refers to the state in which the battery system is ready but has not yet been put into operation. In this mode, the system may need to keep the battery at a suitable temperature to respond to operation instructions at any time, but the urgency requirement for heating is relatively low, and the maintenance heating can be focused on. The regular operation mode refers to the state in which the battery system is stably operated under normal load. In this mode, the battery power output is relatively stable, and the heating strategy can focus on maintaining the battery temperature in the optimal range and taking into account the energy consumption efficiency. The high-load operation mode refers to the state in which the battery system needs to provide high power output in a short time. In this mode, the battery needs to meet the high power demand, and the temperature requirement is high. The heating strategy should ensure that the battery quickly reaches and maintains the optimal working temperature to avoid the impact of low temperature on power output. The emergency state mode refers to the state in which the battery system needs to immediately enter protection or degraded operation due to sudden situations such as serious lack of power, system failure, etc. In this mode, safety is the primary consideration, and the heating strategy should prioritize the basic performance of the battery in extreme situations to ensure that the system can be safely operated or shut down, even if this means sacrificing some heating efficiency or energy consumption. The charging state mode refers to the state in which the battery system is receiving external charging. In this mode, the temperature management of the battery is crucial for charging efficiency and safety, and the heating strategy should be dynamically adjusted according to the charging current, voltage, and battery temperature to optimize the charging process and prevent overheating. The storage state mode refers to the state in which the battery system is idle for a long time. In this mode, the main goal of the heating strategy is to prevent the battery from being damaged due to low temperature, while trying to reduce energy consumption. Heating may only be started to maintain the health of the battery when the battery temperature is too low.

[0066] By explicitly refining the working modes of lithium battery equipment into specific states such as standby, regular operation, high-load operation, emergency state, charging state, storage state, etc., the application can enable the heating control system to more accurately identify the current operating environment and task requirements of the battery system. In different working modes, the system has different requirements for the power output, energy consumption and safety of the battery. For example, in high-load operation or emergency state mode, the battery needs to provide instantaneous high power or ensure safety, at which time the heating strategy will more actively raise the battery temperature to the optimal working range; while in standby or storage mode, it may focus more on maintaining temperature and optimizing energy consumption. This refined mode identification enables the system to more reasonably adjust the weight coefficients or heating start threshold in the heating trigger formula according to the current working mode information, thereby generating a heating enable value that better meets actual needs. This not only improves the performance and safety of the battery in low-temperature environments, but also avoids unnecessary excessive heating, effectively prolongs the service life of the battery, and optimizes overall energy consumption management.

[0067] In some embodiments of the application described above, the internal temperature of each region of the battery pack is calculated by an electro-thermal coupling model, and a temperature difference value is obtained by comparing the internal temperature with the target temperature as the basis for subsequent heating control. However, in actual application, accurate and real-time acquisition of the internal temperature of the battery is a key challenge for battery thermal management. If the electro-thermal coupling model is too simplified, it may lead to inaccurate internal temperature estimation, affecting the effectiveness of heating control; if the model is too complex, it may increase the computational burden, making it difficult to meet the real-time requirements of the battery pack during operation. Therefore, how to efficiently and accurately acquire the internal temperature of the battery is an important prerequisite for refined thermal management.

[0068] To this end, the application further proposes step S200, inputting the voltage value, current value, surface temperature and pre-stored battery thermal resistance parameter of each region of the battery pack into a pre-set electro-thermal coupling model to calculate the internal temperature of the battery pack in each region, and obtaining a temperature difference value by comparing the internal temperature with a target temperature.

[0069] Step S210, based on the current value and voltage value of the battery pack in each region, calculate the heat generation power at the current time;

[0070] Step S220, input the heat generation power, surface temperature and pre-stored battery thermal resistance parameter into a pre-set electro-thermal coupling model to generate the internal temperature, and compare the internal temperature with a pre-set target working temperature to obtain the temperature difference value.

[0071] Specifically, in calculating the heat generation power at the current time, this step aims to quantify the heat generated by the battery during operation due to the internal resistance effect. It can be calculated by monitoring the current and voltage values of each region of the battery in real time, combined with the equivalent internal resistance of the battery. For example, the heat generation power can be estimated according to Ohm's law and Joule's law, through the battery's terminal voltage, current, and open-circuit voltage, or directly calculated by the square of the current multiplied by the equivalent internal resistance. Among them, the equivalent internal resistance can be calibrated by experiment in advance and stored as a lookup table or function model, and its value may vary with the state of charge, state of health, and temperature of the battery. Accurate calculation of heat generation power is the basis for accurate estimation of the internal temperature of the battery, ensuring the reliability of the input data of the electro-thermal coupling model.

[0072] Subsequently, the heat generation power, the surface temperature, and the pre-stored battery thermal resistance parameter are input into the electro-thermal coupling model to generate the internal temperature. The electro-thermal coupling model is a simplified but efficient battery electro-thermal model that abstracts the complex thermal behavior of the battery into a few key parameters such as thermal resistance and thermal capacity, and assumes that heat is mainly transferred in one direction (e.g. from the battery core to the surface). This model can estimate the internal temperature of the battery in real time by taking the above calculated heat generation power, battery surface temperature, and pre-stored battery thermal resistance parameter as input. For example, the model can be represented in the form of a differential equation, which is solved by numerical integration or discretization method, so as to update the internal temperature at each time step. This model can better reflect the dynamic changes of the internal temperature of the battery while ensuring the calculation efficiency.

[0073] Finally, the internal temperature is compared with the pre-set target working temperature to obtain the temperature difference value. This step aims to quantify the deviation between the internal temperature of the battery and the ideal working state. The pre-set target working temperature is usually determined according to the recommended value of the battery manufacturer or the optimal performance temperature range verified by experiment, for example 25℃. By comparing the real-time estimated internal temperature with the target working temperature, a temperature difference value can be obtained. This temperature difference value directly reflects whether the current thermal state of the battery deviates from the optimal working point, providing a direct quantitative basis for subsequent heating decisions.

[0074] By the technical solution, the application provides a more accurate and efficient battery internal temperature estimation method, thereby effectively solving the problem that the battery internal temperature is difficult to be accurately obtained in real time in a low-temperature environment of the battery pack. Specifically, by calculating the heat generation power in real time based on the current value and the voltage value, the heat generation of the battery in the actual working state can be accurately reflected, thereby providing reliable input for the electro-thermal coupling model. On this basis, the heat generation power, the directly measurable surface temperature and the pre-stored battery thermal resistance parameter are input into the electro-thermal coupling model. The model has the characteristics of high calculation efficiency and easy parameter calibration, and can accurately estimate the internal temperature of the battery on the premise of ensuring real-time. The accurate estimation of the internal temperature avoids the hysteresis and inaccuracy caused by relying only on the surface temperature for judgment, so that the subsequent temperature difference value can more truly reflect the actual thermal state of the battery. Therefore, the heating triggering decision based on the temperature difference value will be more accurate and timely, which can ensure that the battery quickly reaches and maintains the optimal working temperature range in a low-temperature environment, thereby significantly improving the low-temperature performance of the battery pack, prolonging the service life thereof and ensuring the working safety.

[0075] In some embodiments of the application, the battery pack is divided into multiple thermal management regions, and independent heating circuits and temperature sensors are configured for each region, so as to obtain the voltage value, current value, surface temperature and other information of each region, and input them into a preset electro-thermal coupling model to calculate the internal temperature of the battery pack, and determine the temperature difference value according to the comparison result of the internal temperature and the target temperature, thereby generating a heating enable value based on the temperature difference value, the state of charge, the state of health and the current working mode information to activate the corresponding heating circuit. However, in actual application, the specific form and parameter selection of the electro-thermal coupling model are crucial for accurately estimating the internal temperature of the battery. If the model is not accurate enough, it may cause deviation in the estimation of the internal temperature, thereby affecting the effectiveness of the heating strategy, causing insufficient heating or excessive heating, and thereby affecting the performance and life of the battery.

[0076] To this end, the application further provides that the electro-thermal coupling model comprises:

[0077]

[0078] wherein, is the internal temperature of the i-th region, is the surface temperature of the i-th region, is the calibrated heat capacity, is the battery thermal resistance parameter, is the heat generation power of the i-th region.

[0079] This electro-thermal coupling model is a mathematical expression used to describe the interaction between the electrical and thermal behaviors of a battery during charging and discharging. Its core objective is to accurately estimate the internal temperature of the battery, which is difficult to measure directly, using measurable external parameters and known battery characteristics. This model reflects the generation, conduction, and dissipation of heat within the battery, providing crucial internal temperature information for battery thermal management. The model can be implemented as a system of differential equations solvable in the control unit using numerical or analytical methods. Its parameters, such as calibrated heat capacity... and battery thermal resistance parameters This is usually obtained by calibrating the battery using experimental data under different operating conditions and temperatures.

[0080] Specifically, in the formula This represents the internal temperature of region i, which is the main output of the model. It represents the temperature at the core of the battery's electrochemical reaction region and is crucial for evaluating battery performance and safety. The surface temperature of region i is represented by a measurable input parameter, typically obtained by a temperature sensor placed on the battery surface, serving as a boundary condition for heat conduction. This represents the calibrated heat capacity, a parameter that indicates the thermal inertia of a battery region and the amount of heat required to raise its temperature. It is a material property that can be determined using calorimetry or other thermal characterization methods. This represents the battery's thermal resistance parameter, which quantifies the resistance to heat flow from the core to the surface within the battery region. Higher thermal resistance means lower efficiency in heat transfer from the core to the surface. It can be determined through thermistor spectroscopy or transient thermal response testing. represents the heat generation power of the i-th region, which represents the total heat generated within the battery region due to internal resistance (Joule heating) and entropy heating effects. According to the above implementation, this heat generation power is calculated based on current and voltage values.

[0081] The electro-thermal coupling model is specifically defined as follows:

[0082]

[0083] This application provides an accurate and physically-based method for estimating the internal temperature of each battery region. This specific electro-thermal coupling model integrates calibrated heat capacity... and battery thermal resistance parameters It can be based on measurable surface temperature and the calculated heat production power Accurately calculate the internal temperature This precision is crucial because the internal temperature, rather than just the surface temperature, is the key indicator of the true thermal state of the battery and its performance in low-temperature environments. Therefore, subsequent heating trigger decisions based on this precise internal temperature will be more reliable and effectively respond to the actual thermal needs of the battery, avoiding under-heating (which can damage the battery) and over-heating (which wastes energy and can be detrimental to the battery). This ensures that the heating strategy can best adapt to the internal thermal conditions of the battery, thereby improving the performance, safety, and service life of the battery in low-temperature environments.

[0084] In some of the above embodiments, although the heating enable value is generated based on the temperature difference value, state of charge, state of health, and working mode information to activate the heating loop, these multi-dimensional information may, when directly input into the preset heating trigger formula, have a too large difference in dimension and value range, leading to distorted calculation results and making it difficult to accurately reflect the true heating needs and risks of the battery, thereby affecting the accuracy and reliability of the heating decision.

[0085] To this end, the present application further proposes, in step S400, based on the temperature difference value, state of charge, state of health, and current working mode information in each region of the battery pack, inputting into the preset heating trigger formula to generate the heating enable value corresponding to each region, so that when the heating enable value of a certain region is greater than or equal to the preset heating start threshold, the heating loop in the corresponding region is activated. The step specifically includes the following operations:

[0086] Step S410, inputting the temperature difference value, state of charge, and state of health in the i-th region of the battery pack into the corresponding preset heating trigger formula to obtain the normalized thermal demand factor, power risk factor, and aging compensation factor;

[0087] Step S420, according to the current working mode, retrieving the weight coefficients corresponding to the thermal demand factor, power risk factor, and aging compensation factor from the pre-stored weight table, and generating the heating enable value in the i-th region by weighted summation of the normalized thermal demand factor, power risk factor, and aging compensation factor;

[0088] Step S430, if the heating enable value is greater than or equal to the preset heating start threshold, activating the heating loop in the i-th region.

[0089] First, the temperature difference value, state of charge, and state of health of the i-th region of the battery pack are input into the corresponding normalized nonlinear mapping function formula to obtain the standardized thermal demand factor, power risk factor, and aging compensation factor. The role of the normalized nonlinear mapping function formula is to convert input parameters of different dimensions and different numerical ranges (such as temperature difference value, state of charge, and state of health) into unified, dimensionless standardized factors. This conversion can effectively eliminate the dimensional differences between the original data and introduce nonlinear characteristics, so that the influence of these factors on the heating enable value can more flexibly and accurately reflect the actual needs and risks of the battery under different operating points. For example, the temperature difference value may be from 0 to several tens of degrees Celsius, the state of charge from 0% to 100%, and the state of health from 0% to 100%. Direct addition or multiplication will cause the influence of some parameters to be amplified or reduced. By normalization, they can be mapped to between 0 and 1, or other unified ranges, ensuring their comparability in subsequent calculations. Nonlinear mapping allows the sensitivity of parameters to be adjusted within a certain interval, for example, when the temperature difference is small (e.g., above the target temperature or slightly below the target temperature), the heating demand may not be sensitive, but when the temperature difference is significantly lower than the target temperature, the sensitivity will increase sharply. The normalized nonlinear mapping function formula can take various forms, such as Sigmoid function, Tanh function, ReLU function, or custom segmented function, etc. For the temperature difference value, a function can be designed to output a lower thermal demand factor when the temperature difference is small (e.g., above the target temperature or slightly below the target temperature), and a higher thermal demand factor when the temperature difference is significantly lower than the target temperature. For the state of charge, a function can be designed to output a higher power risk factor when the state of charge is low to prioritize battery protection. For the state of health, a function can be designed to output a higher aging compensation factor when the state of health is poor to extend battery life. The specific parameters and curve shapes of these functions can be calibrated and optimized through experimental data, battery model simulation, or expert experience.

[0090] Secondly, the weight coefficients corresponding to the thermal demand factor, the power risk factor and the aging compensation factor are retrieved from a pre-stored weight table according to the current working mode. The weight coefficients are used to adjust the relative importance of different factors (thermal demand factor, power risk factor, aging compensation factor) in generating the heating enable value. Different working modes (e.g. standby, regular operation, high load operation or emergency state) have different performance requirements and risk tolerance for the battery. For example, in the acceleration or emergency state when the electric vehicle is working, it may be necessary to prioritize the instantaneous power output capability of the battery, so the thermal demand factor may be given a higher weight; while in standby mode, more attention may be paid to the life and energy consumption of the battery, so the power risk factor or the aging compensation factor may have a higher weight. The pre-stored weight table can be a multi-dimensional array or a lookup table, the index of which can be the working mode, and the values in the table are the weight coefficients of the thermal demand factor, the power risk factor and the aging compensation factor under the corresponding working mode. These weight coefficients can be pre-set and stored through offline simulation, actual working test data analysis, battery performance evaluation and safety policy formulation, etc. For example, in the regular operation mode, the thermal demand factor weight can be set to 0.6, the power risk factor weight to 0.3, and the aging compensation factor weight to 0.1; while in standby mode, these weights can be 0.4, 0.4 and 0.2 respectively. The system will obtain the current working mode information in real time during operation, and query the corresponding weight coefficients from the weight table.

[0091] Thirdly, the heating enable value under the i-th region is generated after the weighted sum of the standardized thermal demand factor, power risk factor and aging compensation factor. Weighted sum is a common decision fusion method, which multiplies multiple standardized factors by their respective weight coefficients and then adds the results to obtain a comprehensive evaluation value - the heating enable value. This heating enable value comprehensively reflects the urgency of heating, the risk level and the consideration of life of the battery in the current state. The larger the value, the more urgent the demand for heating or the higher the risk. The calculation of weighted sum can be realized by software algorithm in the processor.

[0092] Finally, if the heating enable value is greater than or equal to the preset heating start threshold, the heating circuit under the i-th region is activated. The preset heating start threshold is a critical value for determining whether heating needs to be started. When the calculated heating enable value reaches or exceeds this threshold, it indicates that the heating demand or risk of the battery has reached a level that requires action to be taken, at which point the system will activate the heating circuit of the corresponding region and begin heating the battery. The setting of this threshold is directly related to the sensitivity and conservatism of the heating strategy. The preset heating start threshold can be a fixed value or can be dynamically adjusted according to environmental temperature, working mode, and other factors. The heating start threshold is usually determined through experimental tests, simulation analysis, and safety specifications to ensure that heating is started in time before the battery performance is compromised, while avoiding unnecessary heating that causes energy waste. Activation of the heating circuit is usually achieved by sending a start signal to the control unit of the heating circuit, such as controlling the power output of the heater through a PWM signal.

[0093] Through the above technical solution, the temperature difference value, state of charge, state of health, and other heterogeneous data of each region of the battery pack are first converted into standardized thermal demand factors, electrical risk factors, and aging compensation factors through a nonlinear mapping function formula, effectively eliminating the differences in the original data dimensions and numerical ranges, and introducing nonlinear response characteristics, so that the influence of each factor on the heating decision can more truly reflect the actual state of the battery. On this basis, the weight coefficients are dynamically retrieved and applied according to the current working mode for weighted summation to generate the heating enable value, so that the heating decision can fully consider the different emphasis on battery performance, safety, and life under different working modes, thereby achieving adaptive adjustment of the heating strategy. When the heating enable value reaches the preset start threshold, the corresponding heating circuit of the region is precisely activated, avoiding heating lag or excessive heating caused by improper data processing or rigid decision-making, significantly improving the heating control accuracy and response speed of the battery in a low-temperature environment, ensuring that the battery always works in the best temperature range in complex and variable working tasks, and thus ensuring the safety and service life of the battery.

[0094] In some embodiments of the present application, based on the temperature difference value, state of charge, state of health, and current working mode information of each region of the battery pack, the heating enable value corresponding to each region is generated by inputting into a preset heating trigger formula, and when the heating enable value of a certain region is greater than or equal to the preset heating start threshold, the heating circuit under the corresponding region is activated. However, if the internal structure of the preset heating trigger formula and the quantization method of each parameter are not clear enough, the calculation of the heating enable value may lack refinement and adaptability, making it difficult to effectively balance the comprehensive effect of different influencing factors on the heating demand, thereby affecting the optimization effect of the heating strategy and the performance of the battery in a low-temperature environment.

[0095] To this end, the application further proposes specific forms of the preset heating trigger formula, which include:

[0096]

[0097] wherein, is a heating enable value, is a temperature difference value under the i-th region, is a state of charge under the i-th region, is a health state under the i-th region, is a thermal demand factor weight, is an electric quantity risk factor weight, is an aging compensation factor weight.

[0098] Specifically, is a heating enable value, which is a quantitative indicator for comprehensively evaluating the heating demand of the i-th region of the battery pack. This value is obtained by weighted calculation on multiple key battery state parameters, and its size directly reflects the heating urgency or priority of the region. When reaches or exceeds the preset starting threshold, it indicates that the region needs to start heating. is a temperature difference value under the i-th region, which refers to the difference between the internal temperature of the i-th region of the battery pack and the target working temperature. This parameter directly quantifies the current low temperature degree of the battery and is the core indicator for measuring thermal demand. The larger the temperature difference, the more urgent the heating demand usually means. is a state of charge under the i-th region, which represents the remaining capacity percentage of the i-th region of the battery pack. In a low temperature environment, the available capacity and power output capability of the battery will decrease significantly, especially at a low state of charge, the battery is more susceptible to low temperature damage. Therefore, is an important parameter for evaluating low temperature risk and heating necessity. is a health state under the i-th region, which reflects the overall performance and aging degree of the i-th region of the battery pack. As the battery ages, its internal impedance may increase, and its sensitivity to low temperature may also increase. is used to compensate for the heating demand of the aging battery, ensuring its safe operation and performance in low temperature. is a thermal demand factor weight, is an electric quantity risk factor weight, are the weights for the aging compensation factors. These weight coefficients are used to adjust the relative importance of temperature difference, state of charge, and state of health in the calculation of the heating enable value. By adjusting these weights, the heating strategy can be flexibly optimized according to specific application scenarios, such as working modes (e.g. standby, regular operation, high load operation, emergency state) or battery characteristics, to balance heating efficiency, battery life, and working performance. For example, in the emergency landing mode, the heat demand factor weight may need to be increased to quickly warm up; while in standby mode, more emphasis may be placed on the power risk factor to protect the battery. 、 、 are the normalized nonlinear mapping functions used to convert the original temperature difference , state of charge , and state of health to normalized factors. These functions can be linear, piecewise linear, or nonlinear, with the purpose of unifying input parameters of different dimensions and ranges to the same scale, and can introduce nonlinear characteristics to better reflect the battery's heating response under different states. For example, when the temperature difference exceeds a certain critical point, the output of

[0099] By introducing the above specific preset heating trigger formula:

[0100]

[0101] The present application can accurately and quantitatively calculate the heating enable value of each region of the battery pack. The formula evaluates the actual heating demand of the battery by processing the three key parameters of temperature difference , state of charge , and state of health through normalized nonlinear mapping functions, and then performing weighted summation according to their respective weight coefficients 、 、 . This structured calculation method overcomes the problem of heating decision ambiguity or single factor dominance that may exist in traditional methods, making the heating strategy more comprehensive in considering the current thermal state, power risk, and aging degree of the battery. Especially when dynamically adjusting the weight coefficients 、 、 under different working modes, this scheme can achieve highly adaptive heating control, ensuring that the battery can maintain in the optimal working temperature range under different operating conditions, effectively avoiding the damage of low temperature to battery performance and life, while optimizing energy consumption, improving the working reliability and safety of the battery in low temperature environment.

[0102] In some embodiments of the application described above, although it is proposed to activate the corresponding heating circuit according to the heating enable value of each region to achieve low-temperature heating management of the battery pack, merely activating the heating circuit cannot guarantee fine control of the heating process, which may lead to low heating efficiency, temperature overshoot or undesirable temperature rise speed, especially in scenarios such as UAV cruising that have strict requirements on battery performance and endurance, and more precise adjustment of heating power is needed to adapt to real-time working conditions.

[0103] To this end, the application further proposes, after the step of inputting the temperature difference value, state of charge, state of health and current working mode information of each region of the battery pack into a preset heating trigger formula to generate a heating enable value corresponding to each region, and activating the heating circuit in the corresponding region when the heating enable value of a certain region is greater than or equal to a preset heating start threshold, the step further comprises

[0104] Step S500, generating an initial duty cycle according to a preset duty cycle lookup table based on the internal temperature and the state of charge;

[0105] Step S600, determining a duty cycle correction amount according to the deviation of the rate of change of the internal temperature from a preset target temperature rise rate;

[0106] Step S700, performing amplitude limiting processing on the initial duty cycle and the duty cycle correction amount to generate a working PWM duty cycle to control the working power of the activated heating circuit.

[0107] Generating an initial duty cycle according to a preset duty cycle lookup table based on the internal temperature and the state of charge. The preset duty cycle lookup table pre-stores the initial PWM duty cycle required by the heating circuit under different combinations of internal temperature and state of charge. Its role is to provide a basic and empirical heating power setting to quickly respond to heating demand. Specifically, through experimental testing or simulation modeling, the required heating power to achieve the target temperature rise rate is determined under different environmental temperatures, initial battery temperatures, states of charge, etc., and is converted into a PWM duty cycle, and then these data are stored in the memory in the form of a two-dimensional or multi-dimensional lookup table. For example, when the internal temperature is low and the state of charge is high, a larger initial duty cycle may be needed to quickly raise the temperature; when the internal temperature is close to the target temperature, a smaller duty cycle may be needed to maintain the temperature.

[0108] On this basis, a duty cycle correction amount is determined according to the deviation of the rate of change of the internal temperature from a preset target temperature rise rate. The rate of change of the internal temperature reflects the current actual temperature rise speed of the battery. The preset target temperature rise rate is the speed at which the system expects the internal temperature of the battery to rise, which is a configurable parameter, for example, it can be set to rise by X degrees Celsius per minute. By comparing the deviation between the actual temperature rise rate (the rate of change of the internal temperature) and the target temperature rise rate, it can be determined whether the current heating power is appropriate. If the actual temperature rise rate is lower than the target, the heating power needs to be increased, thereby determining a positive duty cycle correction amount; on the contrary, if the actual temperature rise rate is too high, the heating power needs to be reduced, thereby determining a negative duty cycle correction amount. The correction amount can be calculated by a PID controller, a fuzzy logic controller or other control algorithm to achieve dynamic adjustment of the heating process.

[0109] Subsequently, the initial duty cycle is superimposed with the duty cycle correction amount and then subjected to limiting processing. Superimposition is the arithmetic sum of the initial duty cycle and the duty cycle correction amount, resulting in a preliminary, un-limited duty cycle value. Limiting processing is to ensure that the generated duty cycle is within a reasonable physical range, avoiding excessively high or low duty cycles that can cause the heating loop to work abnormally or damage the battery. For example, the duty cycle is usually limited between 0% and 100%. In addition, considering the safety, life and working mode and state of charge of the battery, it may also be necessary to set a maximum working duty cycle upper limit to prevent excessive heating in certain working conditions. Limiting processing will ensure that the superimposed duty cycle does not exceed this upper limit, nor does it fall below 0.

[0110] Finally, a working PWM duty cycle is generated to control the working power of the activated heating loop. The duty cycle obtained after limiting processing is the final working PWM duty cycle. This duty cycle signal is sent to the corresponding heating loop controller to drive the heating element through pulse width modulation (PWM) technology. PWM control changes the average power of the heating element by adjusting the width of the pulse, thereby achieving precise control of the heating temperature. For example, the larger the duty cycle, the longer the heating element is powered on, and the larger the average heating power; on the contrary, the smaller the duty cycle, the smaller the average heating power. This way can achieve stepless adjustment of the heating power, ensuring that the battery can work in the best state in a low temperature environment.

[0111] By the above technical solution, after the heating circuit is activated, the system can obtain a reasonable initial heating duty cycle from a preset query table based on the internal temperature and state of charge of the battery, providing a basis for fast start of the heating process. At the same time, by monitoring the rate of change of the internal temperature in real time and comparing it with the preset target temperature rise rate, the duty cycle correction amount is dynamically calculated, so as to finely adjust the heating power. This feedback control mechanism can effectively avoid under-heating or overshooting, ensuring that the battery warms up at the best rate, improving heating efficiency and battery safety. The working PWM duty cycle generated after the limiting processing can accurately control the output power of the heating circuit, so that the battery can quickly reach and maintain an appropriate working temperature in a low temperature environment, significantly improving the endurance and performance stability of the battery in a low temperature environment.

[0112] In some embodiments of the present application, an initial duty cycle is generated based on the internal temperature and state of charge of the battery pack, and a duty cycle correction amount is determined according to the deviation of the internal temperature change rate from the target temperature rise rate, and then the initial duty cycle and the duty cycle correction amount are superimposed and limited to generate a working PWM duty cycle to control the operation of the heating circuit. However, in actual application, if the upper limit value of the limiting processing is fixed or does not fully consider the key factors such as the current working mode of the lithium battery device and the state of charge of the battery pack, it may lead to excessive or insufficient heating power in certain specific working conditions, thereby affecting the safety, life of the battery pack or the overall endurance performance of the device. For example, when the state of charge of the battery is low, excessive heating power may accelerate the consumption of electric quantity; in the critical working mode, unreasonable heating power may occupy the energy used to power the device, causing safety hazards.

[0113] To this end, the present application further proposes step S700, after the limiting processing of the superimposed initial duty cycle and duty cycle correction amount, the generation of the working PWM duty cycle further includes:

[0114] Step S710, set the maximum working duty cycle upper limit based on the current working mode and / or the state of charge;

[0115] Step S720, the limiting processing limits the superimposed duty cycle between 0 and the maximum working duty cycle.

[0116] Specifically, before generating the working PWM duty cycle, the system will first set a maximum working duty cycle upper limit based on the current working mode of the device and / or the state of charge of the battery pack. The device has different demands for battery energy and heating strategies in different working modes. For example, in standby mode, the limit on heating power can be relatively loose (e.g., the maximum working duty cycle is 100%) to quickly raise the battery temperature; while in standby, regular operation, high-load operation, emergency state, charging state or storage state, etc. Key working modes, in order to ensure sufficient power output and device safety, strict limits on heating power are required, and even the maximum working duty cycle upper limit (e.g., the maximum working duty cycle is 75%) can be reduced to prioritize the power required by the device. The upper limit value can be determined by a pre-set lookup table or a rule-based algorithm, where the key values of the lookup table can be a combination of working modes and state of charge, and the corresponding values are the corresponding maximum working duty cycle upper limit. At the same time, the state of charge of the battery pack directly reflects its remaining power. When the state of charge is low, in order to avoid excessive consumption of battery power for heating, thereby affecting the endurance of the device or causing the battery to be over-discharged, a lower maximum working duty cycle upper limit (e.g., the maximum working duty cycle is 50%) can be set. Conversely, when the state of charge is high, the maximum working duty cycle upper limit (e.g., the maximum working duty cycle is 100%) can be allowed to reach the target temperature faster. This setting can be dynamically adjusted according to the real-time state of charge of the battery pack through a pre-set function relationship or piecewise linear interpolation, etc. Subsequently, the limiting process limits the superimposed duty cycle between 0 and the maximum working duty cycle. This limiting process aims to ensure that the final generated working PWM duty cycle is always within a reasonable and safe range. The lower limit of 0 means no heating, which is necessary when the battery temperature has reached the target or there is no need for heating. The upper limit is determined by the dynamically set maximum working duty cycle upper limit, which ensures that the heating power does not exceed the maximum value allowed by the current working mode and state of charge. By comparing the superimposed duty cycle with 0 and the dynamically set maximum working duty cycle upper limit, and taking the value between the two, it is possible to effectively prevent the heating power from being too high or too low, thereby protecting the battery pack and optimizing energy use.

[0117] By the technical solution, a maximum working duty cycle upper limit can be dynamically set based on the current working mode of the lithium battery equipment and / or the state of charge of the battery pack before generating the working PWM duty cycle. The dynamic adjustment of the limiting process makes the upper limit of the heating power no longer fixed, but can intelligently adapt to the actual operating conditions of the lithium battery equipment. Specifically, when the battery state of charge is low or in a high-load operation, emergency state, etc. Key working mode, the heating power can be effectively limited to avoid excessive consumption of valuable power and ensure the safety and endurance of the lithium battery equipment; while when the battery state of charge is sufficient or in standby mode, etc. Non-critical mode, higher heating power can be allowed to quickly and efficiently raise the battery temperature. This not only optimizes the energy management of the battery pack and prevents the impact of overheating or overcooling on battery life, but also significantly improves the safety, reliability and adaptability of the lithium battery equipment in low temperature environment.

[0118] Specifically, taking the lithium battery equipment of the unmanned aerial vehicle as an example, the battery pack BMS first determines the maximum working duty cycle in this region according to the current working mode and state of charge before generating the PWM duty cycle each time. The specific rules can be as follows:

[0119]

[0120] Subsequently, the limiting process limits the superimposed duty cycle between 0 and the maximum working duty cycle.

[0121] In a specific embodiment, taking the lithium battery equipment of the unmanned aerial vehicle as an example, the unmanned aerial vehicle performs a cruise task in a-10℃ environment, and the internal temperature of the battery pack in the third region is estimated by the electro-thermal coupling model to be-3℃, and the state of charge SOC is 45%. The BMS first queries the pre-stored duty cycle query table. The duty cycle query table is calibrated based on a large number of low-temperature heating experiments, and maps the initial PWM duty cycle corresponding to different (internal temperature, SOC) combinations; according to (-3℃, 45%), the initial duty cycle is 38%. Subsequently, the system calculates the internal temperature change rate of this region with a period of 1 second, and the internal temperature change rate of the current sampling window is / dt = 0.9℃ / min, while the preset target heating rate is 1.2℃ / min, the deviation is -0.3℃ / min. Since the deviation is negative and exceeds the dead zone range (±0.1℃ / min), the system determines the duty cycle correction amount to be +5% according to the preset rules (such as piecewise linear rules or fuzzy control rules or table lookup method, specifically, a one-dimensional lookup table of heating rate deviation and duty cycle correction amount is pre-established, and BMS directly interpolates and queries during operation). Superimpose the initial duty cycle 38% and the correction amount +5% to get 43%, and combine the current working mode and SOC=45% to find that the maximum working duty cycle upper limit is 40%, and finally the working PWM duty cycle is output as 40% after limiting processing, thereby dynamically improving the heating power and accelerating the battery temperature rise to the target working interval under the premise of ensuring safety.

[0122] In a low-temperature environment of a lithium battery, although an adaptive PWM heating control method has been proposed to optimize the temperature management of a battery pack, if this method is to be applied to a lithium battery device, it needs specific hardware and software carriers to carry and execute. It is difficult to ensure that it can run stably and efficiently under the limited resources and harsh working conditions of the lithium battery device and achieve accurate heating control of the battery pack, only relying on the description of the method steps.

[0123] For reference Figure 6 To this end, the present application proposes an adaptive PWM heating control device for a lithium battery, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the above-mentioned adaptive PWM heating control method for a lithium battery device in a low-temperature environment.

[0124] Specifically, the adaptive PWM heating control device for a lithium battery device in a low-temperature environment is an integrated system specially designed for a lithium battery device, used for heating management of a battery pack in a low-temperature environment. This device converts the abstract heating control method into a physically operable physical entity, ensuring that the method can be stably and reliably executed on the above-mentioned lithium battery device. Its design needs to fully consider the size, weight, power consumption limitations and harsh environmental adaptability requirements such as shock resistance and low temperature resistance of the lithium battery device, and can be an independent control unit integrated on the main control board of the lithium battery device, or as part of the battery pack (BMS).

[0125] The memory 10 is a hardware component for storing data and instructions. Its main role is to store the computer program of the lithium battery device low-temperature environment adaptive PWM heating control method, preset parameters (such as battery thermal resistance parameters, target temperature, heating start threshold, weight table, duty cycle lookup table, etc.), and various real-time data obtained during device operation (such as voltage value, current value, surface temperature, ambient temperature, state of charge, state of health, working mode information, internal temperature, temperature difference value, heating enable value, etc.). The memory 10 can take the form of flash memory, EEPROM, RAM (such as DDR SDRAM), etc. The specific choice depends on factors such as required storage capacity, read-write speed, power consumption, and cost. For example, program code and fixed configuration parameters are usually stored in non-volatile flash memory, while dynamic data generated during runtime is stored in high-speed RAM.

[0126] The processor 20 is a central processing unit that executes computer program instructions, processes data, and controls operations. Its core role is to execute the computer program stored in the memory 10, thereby implementing all steps of the above method. This includes but is not limited to obtaining sensor data, running an electro-thermal coupling model, calculating a temperature difference value, executing a heating trigger formula, generating a heating enable value, determining whether to activate a heating loop, and generating a working PWM duty cycle, etc. The processor 20 can be a microcontroller (MCU), a digital signal processor (DSP), or an embedded processor (such as an ARM Cortex series), which needs to be selected considering processing power, power consumption, real-time requirements, and integration capabilities with other systems of the lithium battery device. For example, a high-performance MCU can simultaneously handle data acquisition, complex model calculation, and precise PWM signal output.

[0127] The computer program stored on the memory 10 and executable on the processor 20 is a collection of instructions that are read and executed by the processor 20 to complete specific tasks. It is the logical core of the lithium battery device low-temperature environment adaptive PWM heating control method. Through this program, the processor 20 can intelligently manage the battery pack according to the predetermined algorithm and process. The program is usually written in embedded programming languages such as C / C++ and is compiled and burned into the memory 10. The program includes data acquisition modules, model calculation modules, decision logic modules, PWM output control modules, etc. to implement all steps of the above method. For example, the program will define functions to read temperature sensor data, implement the calculation logic of the electro-thermal coupling model, and dynamically adjust the duty cycle of the PWM signal according to the calculation results.

[0128] By providing an adaptive PWM heating control device applied to a lithium battery device, the device comprises a memory 10, a processor 20, and a computer program stored on the memory 10 and executable on the processor 20, so that the above-mentioned lithium battery device low-temperature environment adaptive PWM heating control method can be reliably and efficiently deployed and executed in the actual lithium battery device. The processor 20 can obtain the voltage value, current value, surface temperature and other key data of each region of the battery pack in real time, and quickly execute complex algorithms such as electric-thermal coupling model calculation, heating trigger formula judgment and PWM duty cycle generation, to ensure the accurate activation and control of the heating loop. The memory 10 provides a stable storage space for the computer program and running data. This combination of software and hardware device not only converts abstract control logic into an operable physical entity, but more importantly, it ensures that the heating control method can be run with high real-time performance and high reliability, thereby realizing fine and adaptive management of the battery pack temperature and effectively improving the endurance, battery performance and safety of the lithium battery device in low-temperature environment.

[0129] In the low-temperature working environment of the lithium battery device, in order to realize adaptive PWM heating control of the battery pack, a set of precise control method is needed. This method involves complex steps such as multi-parameter acquisition, electric-thermal coupling model calculation, heating trigger formula generation and PWM duty cycle adjustment. However, if this method only stays at the theoretical level, it will be difficult to deploy and run in the actual lithium battery device. How to effectively solidify these complex control logic and calculation steps and make them stable and efficient on the hardware platform is the key challenge to realize the practicality of the method.

[0130] To this end, the present application provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by the processor 20 to realize the steps of the above-mentioned lithium battery device low-temperature environment adaptive PWM heating control method.

[0131] In particular, the storage medium is a physical device for storing digital data or computer programs. Its role is to provide a non-volatile or volatile space to persistently or temporarily save information, ensuring that the computer program remains after power failure or is quickly accessed during runtime. Common storage media include, but are not limited to, flash memory, solid state disk, hard disk drive, read-only memory 10, random access memory 10, etc. The computer-readable storage medium refers to a medium that can be read and interpreted by the computer system or processor 20 to store data and instructions therein. Such a medium can be physical, non-transitory, such as the aforementioned flash memory, solid state disk, etc., or temporary, such as random access memory 10. Its core is to be able to carry a computer program and allow the processor 20 to access and execute it. The computer program is a collection of instructions arranged in a predetermined logical order, designed to guide the processor 20 to perform specific tasks or implement specific functions. In this application, the computer program contains all the steps and logic of the above-mentioned lithium battery device low-temperature environment adaptive PWM heating control method, such as data acquisition, model calculation, decision-making judgment and control output, etc. The processor 20 is the core component of the computer system, responsible for interpreting and executing instructions in the computer program. It processes data by performing arithmetic, logic, control and input / output operations. In this application, the processor 20 will read the computer program on the storage medium, and according to the program instructions, it will acquire battery data in real time, perform complex electro-thermal coupling calculations, judge heating requirements and output corresponding PWM control signals, thereby realizing adaptive heating control of the lithium battery device.

[0132] By storing the above-mentioned lithium battery device low-temperature environment adaptive PWM heating control method in the form of a computer program on a computer-readable storage medium and executing it by the processor 20, this application can convert the abstract control method into an actually executable software entity. This allows the complex battery thermal management logic to be solidified, ensuring the consistency and repeatability of the control strategy. The processor 20 can efficiently and accurately perform various calculations and judgments in the program, such as real-time acquisition of voltage values, current values, surface temperatures, etc. of each region of the battery pack, and calculation of internal temperature based on the electro-thermal coupling model, and then generation of heating enable value according to the heating trigger formula, and finally output of PWM duty cycle to control the heating loop. This implementation not only improves the response speed and accuracy of the control system, but also facilitates the deployment, update and maintenance of the program, thereby effectively solving the problems of method practicality and system integration, ensuring the safe and efficient operation of the lithium battery device in low-temperature environments.

[0133] Through the above-mentioned sub-area, multi-parameter fusion, and adaptive PWM heating control method, the lithium battery equipment can realize accurate thermal management of each region of the battery pack in a low-temperature environment. This effectively solves the problems of lithium-ion battery resistance increase, capacity drop, lithium precipitation, and the like in low temperature, and avoids the problems of local overheating, insufficient heating, and high energy consumption in the prior art, thereby comprehensively improving the operation reliability of the lithium battery equipment in a severe cold environment and the service life of the battery pack. Each technical feature of the above-described embodiments can be combined arbitrarily. In order to make the description simple, each technical feature in the above-described embodiments is not described in all possible combinations, however, as long as the combination of these technical features does not exist contradictory, it should be considered as the scope of the present disclosure.

[0134] The above-described embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of protection of the present application patent should be subject to the appended claims.

Claims

1. A method for adaptive PWM heating control in low-temperature environments for lithium battery devices, characterized in that, The method includes: The battery pack is divided into N thermal management zones, and each zone is equipped with an independent and controllable heating circuit and temperature sensor. The system acquires information on voltage, current, surface temperature, ambient temperature, state of charge, health status, and current operating mode for each region of the battery pack. The voltage, current, surface temperature, and pre-stored battery thermal resistance parameters of each region of the battery pack are input into a preset electro-thermal coupling model to calculate the internal temperature of each region of the battery pack, and the temperature difference is obtained by comparing the internal temperature with the target temperature. Based on the temperature difference, state of charge, health status and current operating mode information of each region of the battery pack, the information is input into a preset heating trigger formula to generate a heating enable value for each region. When the heating enable value of a certain region is greater than or equal to a preset heating start threshold, the heating circuit in the corresponding region is activated.

2. The low-temperature environment adaptive PWM heating control method for lithium battery equipment according to claim 1, characterized in that, The operating modes include one or more of the following: standby, normal operation, high load operation, emergency state, charging state, and storage state.

3. The low-temperature environment adaptive PWM heating control method for lithium battery equipment according to claim 1, characterized in that, The step of inputting the voltage, current, surface temperature, and pre-stored battery thermal resistance parameters of each region of the battery pack into a preset electro-thermal coupling model to calculate the internal temperature of each region of the battery pack, and then calculating the temperature difference value by comparing the internal temperature with the target temperature, includes: Calculate the heat generation power at the current moment based on the current value and voltage value of the battery pack in each region; The heat generation power, the surface temperature, and the pre-stored battery thermal resistance parameters are input into a preset electro-thermal coupling model to generate the internal temperature, and the internal temperature is compared with the preset target operating temperature to obtain the temperature difference value.

4. The low-temperature environment adaptive PWM heating control method for lithium battery equipment according to claim 1, characterized in that, The step of inputting the temperature difference, state of charge, health status, and current operating mode information of each region of the battery pack into a preset heating trigger formula to generate a heating enable value corresponding to each region, so as to activate the heating circuit in the corresponding region when the heating enable value of a certain region is greater than or equal to a preset heating start threshold, includes: Input the temperature difference, state of charge, and health status of the battery pack into the corresponding preset heating trigger formula to obtain the standardized heat demand factor, energy risk factor, and aging compensation factor in the i-th region of the battery pack. According to the current working mode, the weight coefficients corresponding to the heat demand factor, power risk factor and aging compensation factor are retrieved from the pre-stored weight table. After weighted summation based on the standardized heat demand factor, power risk factor and aging compensation factor, the heating enable value under the i-th region is generated. If the heating enable value is greater than or equal to the preset heating start threshold, the heating circuit in the i-th region is activated.

5. The low-temperature environment adaptive PWM heating control method for lithium battery equipment according to claim 1, characterized in that, The step of inputting the temperature difference, state of charge, health status, and current operating mode information of each region of the battery pack into a preset heating trigger formula to generate a heating enable value corresponding to each region, so that when the heating enable value of a certain region is greater than or equal to a preset heating start threshold, the heating circuit in the corresponding region is activated, further includes: Based on the internal temperature and the state of charge, an initial duty cycle is generated according to a preset duty cycle lookup table; The duty cycle correction amount is determined based on the deviation between the rate of change of the internal temperature and the preset target heating rate. The initial duty cycle and the duty cycle correction amount are superimposed and then subjected to amplitude limiting to generate the working PWM duty cycle, so as to control the working power of the activated heating circuit.

6. The low-temperature environment adaptive PWM heating control method for lithium battery equipment according to claim 5, characterized in that, After superimposing the initial duty cycle and the duty cycle correction amount and performing amplitude limiting, the step of generating the working PWM duty cycle before the following steps further includes: Set the maximum duty cycle limit based on the current operating mode and / or the state of charge; The limiting process restricts the superimposed duty cycle to between 0 and the maximum working duty cycle.

7. An adaptive PWM heating control lithium battery device, characterized in that, The heating control device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the lithium battery device low-temperature environment adaptive PWM heating control method as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the lithium battery device low-temperature environment adaptive PWM heating control method as described in any one of claims 1 to 6.

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