A 1.5V lithium-ion battery thermal management method and system based on temperature strain analysis

Abstract

The present invention relates to the field of energy storage, and proposes a 1.5V lithium-ion battery thermal management method based on temperature strain analysis, including: real-time monitoring of battery surface temperature and acquisition of temperature strain data, extraction of key sequence values ​​to analyze strain trends, and risk assessment to obtain corresponding risk levels and risk indexes, based on the risk index, assessing battery status and performing heat load assessment, querying corresponding heat impact factors, marking key impact areas, determining heat management paths, calculating path transfer amounts and heat transfer efficiency, and optimizing operating parameters to improve heat drop ratios, extracting multi-thermal energy characteristics, querying hotspot distribution states, analyzing hotspot labels, and generating thermal management solutions for batteries. The present invention can improve the thermal management level of 1.5V lithium-ion batteries.

Description

Technical Field

[0001] The present invention relates to the field of energy storage, and in particular to a 1.5V lithium-ion battery thermal management method and system based on temperature strain analysis. Background Art

[0002] In today's energy storage field, with the widespread application of portable electronic devices and electric vehicles, 1.5V lithium-ion batteries have attracted much attention due to their high energy density, long cycle life and other advantages, and play a key role in various electronic devices and power systems.

[0003] At present, the traditional 1.5V lithium-ion battery thermal management method mainly adopts air cooling or liquid cooling. On the one hand, the heat dissipation effect of air cooling is limited, and it is difficult to meet the heat dissipation requirements during high-power discharge, which easily leads to excessive battery temperature and affects battery performance and life; on the other hand, although the liquid cooling method has a better heat dissipation effect, the system is complex, the cost is high, and there is a risk of leakage. In addition, the traditional thermal management method mainly relies on experience and simple temperature monitoring, lacks in-depth management of the temperature strain inside the battery, therefore, a 1.5V lithium-ion battery thermal management method based on temperature strain analysis is needed to improve the thermal management level of 1.5V lithium-ion batteries. Summary of the invention

[0004] The present invention provides a 1.5V lithium ion battery thermal management method and system based on temperature strain analysis, the main purpose of which is to improve the thermal management level of the 1.5V lithium ion battery.

[0005] To achieve the above object, the present invention provides a 1.5V lithium-ion battery thermal management method based on temperature strain analysis, comprising:

[0006] Obtain a 1.5V lithium-ion battery, monitor the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, collect a temperature strain data sequence corresponding to the battery surface temperature, and extract a key sequence value in the temperature strain data sequence;

[0007] Analyze the strain trend corresponding to the key sequence value, perform risk assessment on the strain trend to obtain risk assessment data, classify the risk assessment data to obtain a risk assessment level, and calculate the risk index corresponding to the risk assessment level;

[0008] Based on the risk index, analyzing the battery state corresponding to the 1.5V lithium-ion battery, performing a heat load assessment on the battery state to obtain a heat load assessment value, querying a heat impact factor corresponding to the assessment heat load value, and marking a key impact area corresponding to the heat impact factor;

[0009] Based on the key impact area, determining a thermal management path corresponding to the 1.5V lithium-ion battery, analyzing a path transfer amount corresponding to the thermal management path, calculating a heat transfer efficiency corresponding to the thermal management path based on the path transfer amount, optimizing operating parameters corresponding to the 1.5V lithium-ion battery based on the heat transfer efficiency, and calculating a heat drop ratio corresponding to the operating parameters;

[0010] Based on the heat drop ratio, the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery are extracted. Based on the multi-thermal energy characteristics, the hotspot distribution state inside the 1.5V lithium-ion battery is queried, the hotspot tags corresponding to the hotspot distribution state are analyzed, and based on the hotspot tags, a thermal management solution corresponding to the 1.5V lithium-ion battery is generated.

[0011] Optionally, extracting a key sequence value from the temperature strain data sequence includes:

[0012] Segmenting the temperature strain data sequence to obtain segmented subsequences;

[0013] identifying sequence fluctuation segments in the segmented subsequences;

[0014] Performing peak detection on the data in the fluctuation segment of the sequence to obtain a peak point set;

[0015] Analyze the time intervals corresponding to the peak points in the peak point set;

[0016] Based on the time interval, a key sequence value in the temperature strain data sequence is extracted.

[0017] Optionally, performing risk assessment on the strain trend to obtain risk assessment data includes:

[0018] analyzing a strain rate corresponding to the strain trend;

[0019] Based on the strain rate, dividing the risk level range corresponding to the strain trend;

[0020] Query the risk characteristic indicators of different levels corresponding to the risk level range;

[0021] Based on the risk characteristic index, a risk assessment is performed on the strain trend to obtain risk assessment data.

[0022] Optionally, calculating the risk index corresponding to the risk assessment level includes:

[0023] The risk index corresponding to the risk assessment level is calculated using the following formula:

[0024]

[0025] in, represents the risk index corresponding to the risk assessment level, represents the weight coefficient of the temperature assessment value corresponding to the risk assessment level, Indicates the number of temperature assessment values ​​corresponding to the risk assessment level, Indicates the index of the temperature evaluation value, Indicates The battery surface temperature corresponding to the temperature evaluation value is Represents the average value of the battery surface temperature, Indicates the temperature range of the battery surface temperature, represents the weight coefficient of the strain trend value corresponding to the risk assessment level, represents the number of strain trend values, represents the index of the strain trend value, Indicates A quantitative indicator of the strain trend value, Represents the strain range of the strain eigenvalues.

[0026] Optionally, performing thermal load assessment on the battery state to obtain a thermal load assessment value includes:

[0027] Analyzing the temperature distribution area corresponding to the battery state;

[0028] Determine a high temperature area and a low temperature area corresponding to the temperature distribution area;

[0029] Calculating a heat flux density corresponding to the battery state based on the high temperature region and the low temperature region;

[0030] According to the heat flux density, evaluating a heat generation rate corresponding to the battery state;

[0031] Based on the heat generation rate, a heat load evaluation is performed on the battery state to obtain a heat load evaluation value.

[0032] Optionally, marking the key impact area corresponding to the heat impact factor includes:

[0033] Analyze the impact degree value corresponding to the thermal impact factor;

[0034] Based on the impact degree value, determining the potential impact area corresponding to the thermal impact factor;

[0035] Monitor regional temperature data corresponding to the potential impact area;

[0036] Evaluating thermal sensitivity corresponding to the regional temperature data;

[0037] Based on the thermal sensitivity, a key impact area corresponding to the thermal impact factor is marked.

[0038] Optionally, the calculating the heat transfer efficiency corresponding to the thermal management path based on the path transfer amount includes:

[0039] The heat transfer efficiency corresponding to the thermal management path is calculated using the following formula:

[0040]

[0041] in, represents the heat transfer efficiency corresponding to the thermal management path, represents the thermal output power corresponding to the thermal management path, represents the number of path nodes corresponding to the thermal management path, Indicates the node index corresponding to the path node, Indicates The transfer heat corresponding to each path node is Indicates The node temperature corresponding to each path node is represents the heat flux density function corresponding to the thermal management path, Indicates the time threshold corresponding to the thermal management path.

[0042] Optionally, calculating the heat drop ratio corresponding to the operating parameter includes:

[0043] The heat drop ratio corresponding to the operating parameters is calculated using the following formula:

[0044]

[0045] in, represents the heat drop ratio corresponding to the operating parameters, represents the number of monitoring points corresponding to the operating parameters, Indicates the number index of monitoring points, represents the average operating temperature corresponding to the operating parameters, Indicates The temperature value of each monitoring point, Indicates The heat flow at each monitoring point, Indicates The thermal resistance value of each monitoring point is represents the number of power points corresponding to the operating parameters, Indicates the number index of power points, Indicates The power output value corresponding to each power point is: Indicates The state time corresponding to each power point.

[0046] Optionally, extracting the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery based on the heat drop ratio includes:

[0047] Analyzing the correlation between the heat drop ratio and the battery temperature corresponding to the 1.5V lithium-ion battery;

[0048] Based on the association relationship, the temperature range data corresponding to the 1.5V lithium-ion battery is screened;

[0049] extracting temperature flow characteristics from the temperature interval data;

[0050] Determining a heat conduction mode corresponding to the 1.5V lithium-ion battery according to the temperature flow characteristics;

[0051] Based on the heat conduction mode, multiple thermal energy characteristics corresponding to the 1.5V lithium-ion battery are extracted.

[0052] Optionally, in order to solve the above problem, the present invention provides a 1.5V lithium-ion battery thermal management system based on temperature strain analysis, the system comprising:

[0053] A sequence value extraction module is used to obtain a 1.5V lithium-ion battery, monitor the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, collect a temperature strain data sequence corresponding to the battery surface temperature, and extract a key sequence value from the temperature strain data sequence;

[0054] An indicator calculation module is used to analyze the strain trend corresponding to the key sequence value, perform risk assessment on the strain trend to obtain risk assessment data, classify the risk assessment data to obtain a risk assessment level, and calculate a risk index corresponding to the risk assessment level;

[0055] A region marking module is used to analyze the battery state corresponding to the 1.5V lithium-ion battery based on the risk index, perform a heat load assessment on the battery state to obtain a heat load assessment value, query a heat impact factor corresponding to the assessment heat load value, and mark a key impact region corresponding to the heat impact factor;

[0056] a heat drop ratio calculation module, for determining a thermal management path corresponding to the 1.5V lithium-ion battery based on the key impact area, analyzing a path transfer amount corresponding to the thermal management path, calculating a heat transfer efficiency corresponding to the thermal management path based on the path transfer amount, optimizing an operating parameter corresponding to the 1.5V lithium-ion battery based on the heat transfer efficiency, and calculating a heat drop ratio corresponding to the operating parameter;

[0057] A solution generation module is used to extract the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery based on the heat drop ratio, query the hot spot distribution state inside the 1.5V lithium-ion battery based on the multi-thermal energy characteristics, analyze the hot spot tags corresponding to the hot spot distribution state, and generate a thermal management solution corresponding to the 1.5V lithium-ion battery based on the hot spot tags.

[0058] First, the present invention obtains a 1.5V lithium-ion battery and monitors the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, so as to timely grasp the working status of the battery and avoid adverse effects on battery performance and life due to excessively high or low temperature. In addition, understanding the trend of battery surface temperature changes can provide an important basis for optimizing battery use and management. At the same time, the present invention helps to predict in advance possible overheating or overcooling of the battery by analyzing the strain trend corresponding to the key sequence value, and provides a basis for subsequent thermal management measures to ensure that the battery operates in a safe and efficient state. Based on the risk index, the present invention analyzes the battery status corresponding to the 1.5V lithium-ion battery, and can timely discover whether the battery is in an overheated, overcooled or other abnormal state, and can timely take measures such as heat dissipation to prevent battery damage or danger. Secondly, it helps to optimize the use and management strategy of the battery. The present invention determines the thermal management path corresponding to the 1.5V lithium-ion battery based on the key impact area, analyzes the path transfer amount corresponding to the thermal management path, and can plan the thermal management path in a targeted manner to ensure that heat can be effectively dissipated from these key parts that are prone to thermal problems or imported when needed, so that the temperature distribution of the battery is more uniform, minimizes local overheating, and ensures the stable performance of the battery. Furthermore, based on the heat drop ratio, the present invention extracts the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery, which can reveal the inherent laws of heat generation, transfer and dissipation of the battery under various operating conditions, and can be optimized in a targeted manner during battery material selection and structural design to reduce heat generation or improve heat dissipation efficiency. Therefore, the 1.5V lithium-ion battery thermal management method and system based on temperature strain analysis proposed in the present invention can improve the thermal management level of 1.5V lithium-ion batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic flow chart of a 1.5V lithium-ion battery thermal management method based on temperature strain analysis provided by one embodiment of the present invention;

[0060] Figure 2 A schematic diagram of a module for implementing a 1.5V lithium-ion battery thermal management system based on temperature strain analysis provided in one embodiment of the present invention.

[0061] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0063] The embodiment of the present application provides a 1.5V lithium-ion battery thermal management method based on temperature strain analysis. The execution subject of the 1.5V lithium-ion battery thermal management method based on temperature strain analysis includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the 1.5V lithium-ion battery thermal management method based on temperature strain analysis can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0064] Embodiment 1:

[0065] Reference Figure 1 FIG. 1 is a flow chart of a 1.5V lithium-ion battery thermal management method based on temperature strain analysis provided by an embodiment of the present invention. In this embodiment, the 1.5V lithium-ion battery thermal management method based on temperature strain analysis includes:

[0066] S1. Obtain a 1.5V lithium-ion battery, monitor the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, collect a temperature strain data sequence corresponding to the battery surface temperature, and extract a key sequence value in the temperature strain data sequence.

[0067] The present invention obtains a 1.5V lithium-ion battery and monitors the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, so as to timely grasp the working state of the battery, avoid adverse effects on battery performance and life due to excessively high or low temperature, and understand the change trend of the battery surface temperature, which can provide an important basis for optimizing the use and management of the battery.

[0068] Among them, the 1.5V lithium-ion battery refers to a lithium-ion battery with a specific voltage of 1.5 volts, which plays a key role in portable electronic devices and power systems by virtue of its advantages such as high energy density and long cycle life; the battery surface temperature refers to the temperature of the surface of the 1.5V lithium-ion battery shell. This temperature parameter can intuitively reflect the working state and thermal conditions of the battery. Optionally, the real-time monitoring of the battery surface temperature corresponding to the 1.5V lithium-ion battery can be achieved through a temperature sensor module, such as: DS18B20 digital temperature sensor module, which can output the temperature data of the battery surface in real time.

[0069] Furthermore, the present invention collects the temperature strain data sequence corresponding to the battery surface temperature, which helps to analyze the thermal characteristics of the battery and provide accurate data support for optimizing battery design and thermal management strategies. The temperature rise rate of the battery when working under high load can be determined based on the temperature strain data sequence, thereby improving the heat dissipation measures in a targeted manner.

[0070] Among them, the temperature strain data sequence refers to a group of data arranged in chronological order obtained by continuously monitoring the surface temperature of a 1.5V lithium-ion battery within a certain time range. This data sequence reflects the change of the battery surface temperature over time, and includes information such as the high and low values ​​of the temperature, the change trend, and the fluctuation range. Optionally, the acquisition of the temperature strain data sequence corresponding to the battery surface temperature can be achieved through a data acquisition card, such as: connecting the data acquisition card to a temperature sensor, acquiring the battery surface temperature in real time, and storing the data as a temperature strain data sequence.

[0071] Furthermore, the present invention extracts key sequence values ​​from the temperature strain data sequence and analyzes these key values ​​to timely discover abnormal fluctuations or trend changes in battery temperature, thereby taking corresponding thermal management measures in advance to ensure the safe and stable operation of the battery. The key sequence values ​​can present the core content of the data in a more concise form, thereby improving the efficiency of data analysis.

[0072] The key sequence values ​​refer to important and representative values ​​extracted from the temperature strain data sequence, which may be peak points, temperature values ​​at specific time points, extreme values ​​of temperature change rates, etc.

[0073] As an embodiment of the present invention, the extraction of key sequence values ​​in the temperature strain data sequence includes: segmenting the temperature strain data sequence to obtain segmented subsequences; identifying sequence fluctuation segments in the segmented subsequences; performing peak detection on the data in the sequence fluctuation segments to obtain a peak point set; analyzing the time intervals corresponding to the peak points in the peak point set; and extracting the key sequence values ​​in the temperature strain data sequence based on the time intervals.

[0074] Among them, the data segmentation sequence refers to dividing the temperature strain data sequence into several continuous subsequences according to specific rules or standards. These subsequences can be divided according to factors such as time and temperature change amplitude; the sequence fluctuation segment refers to the part in the segmented subsequence where the temperature strain data shows obvious fluctuation changes. This part of the data usually reflects the thermal state changes of the battery within a specific time period; the peak point set refers to the set of local maximum value points of the temperature strain data determined by the peak detection algorithm in the sequence fluctuation segment. These peak points can reflect the highest value of the battery temperature within a specific time period; the time interval refers to the time difference between adjacent peak points in the peak point set, which can understand the frequency and periodicity of the battery temperature change.

[0075] Furthermore, the sequence segmentation of the temperature strain data sequence can be achieved through data processing tools, such as Excel, Python's pandas library and other tools to perform data segmentation operations; the identification of the sequence fluctuation segment in the segmented subsequence can be achieved through a trend change analysis method, such as analyzing the trend change of the subsequence, when there is an obvious upward or downward trend change, this part is the sequence fluctuation segment; the peak detection of the data in the sequence fluctuation segment can be achieved through data visualization tools, such as Tableau, Plotly, etc.; the analysis of the time interval corresponding to the peak point in the peak point set can be achieved through a time series analysis algorithm, such as the autoregressive moving average model (ARMA) can analyze the pattern of the time interval; the extraction of the key sequence value in the temperature strain data sequence can be achieved through a principal component analysis algorithm, such as the main components of the data can be extracted as the key sequence value.

[0076] S2. Analyze the strain trend corresponding to the key sequence value, perform risk assessment on the strain trend to obtain risk assessment data, classify the risk assessment data to obtain a risk assessment level, and calculate the risk index corresponding to the risk assessment level.

[0077] The present invention helps to predict in advance the possible overheating or overcooling of the battery by analyzing the strain trend corresponding to the key sequence value, providing a basis for subsequent thermal management measures, thereby ensuring that the battery operates in a safe and efficient state.

[0078] The strain trend refers to the change direction and law of the key sequence value in the temperature strain data sequence of the 1.5V lithium-ion battery over a period of time, which can be manifested as different states such as the increase, decrease or stability of the temperature strain. Optionally, the analysis of the strain trend corresponding to the key sequence value can be achieved through data analysis tools, such as Excel, SPSS and other tools.

[0079] Furthermore, the present invention performs risk assessment on the strain trend to obtain risk assessment data, which can infer the risk level of overheating, overcooling or other abnormal thermal conditions that the battery may face, so as to take corresponding preventive measures before the problem occurs, thereby ensuring the safe operation of the battery and reducing the probability of battery failure and damage due to thermal problems.

[0080] The risk assessment data refers to the comprehensive data results obtained after risk assessment of strain trends, which includes specific risk level values, risk descriptions, possible impacts, and recommended response measures.

[0081] As an embodiment of the present invention, the risk assessment of the strain trend to obtain risk assessment data includes: analyzing the strain rate corresponding to the strain trend; dividing the risk level range corresponding to the strain trend based on the strain rate; querying risk characteristic indicators of different levels corresponding to the risk level range; and performing risk assessment on the strain trend based on the risk characteristic indicators to obtain risk assessment data.

[0082] Among them, the strain rate refers to the change in strain per unit time, which is used to measure the speed of change of strain trend, and reflects the speed of change of battery temperature strain in the time dimension; the risk level range refers to the interval range of different risk levels divided according to factors such as strain rate. For example, the risk level can be divided into different ranges such as low risk, medium risk and high risk, and each range corresponds to a specific strain rate interval or other related indicator interval; the risk characteristic indicator refers to the relevant elements used to describe the specific state characteristics of lithium-ion batteries under different risk levels (such as low risk, medium risk, high risk, etc.), covering battery temperature range, temperature change stability, battery internal resistance change rate, and whether there are abnormal changes in battery appearance (such as heating, bulging, smoking, etc. of the outer shell).

[0083] Furthermore, the analysis of the strain rate corresponding to the strain trend can be achieved through a sliding window algorithm, such as: setting a window of a fixed size to slide on the strain data sequence, and calculating the strain rate in each window; the division of the risk level range corresponding to the strain trend can be achieved through a hierarchical analysis method, such as: considering factors such as the size and change trend of the strain rate, determining the weights of different factors corresponding to different risk levels through a hierarchical analysis method, and comprehensively judging the risk level range; the query of risk characteristic indicators corresponding to different levels of the risk level range can be achieved through a decision tree algorithm, such as: ID3 or C4.5 decision tree algorithm, constructing a decision tree according to the characteristics of the strain rate and the division conditions of the risk level range, for querying risk characteristic indicators; the risk assessment of the strain trend can be achieved through a scenario analysis method, such as: for a rapidly rising strain trend scenario, analyzing the possibility and severity of the consequences such as battery overheating and performance degradation that may be caused, thereby assessing the risk.

[0084] The present invention classifies the risk assessment data into different levels, such as low risk, medium risk and high risk levels, so that the current risk level faced by the battery can be quickly and intuitively understood without in-depth analysis of specific data details, thereby enabling preliminary decisions and response measures to be made in a timely manner, which is conducive to the evaluation and management of the long-term performance and reliability of the battery.

[0085] Among them, the risk assessment level refers to dividing the risk degree of battery strain trend into different levels or layers based on the analysis and judgment of risk assessment data. It is a classification representation of risk conditions, which is used to intuitively reflect the size and severity of the risks faced by the battery in thermal management. Optionally, the level division of the risk assessment data can be achieved through interval division, such as: dividing the intervals with equal or unequal intervals according to the value range of the risk assessment data.

[0086] Furthermore, the present invention helps to make more scientific and accurate decisions in battery thermal management by calculating the risk index corresponding to the risk assessment level, such as determining the risk level to be prioritized and allocating corresponding resources, and the risk index calculated in real time can promptly reflect the changing trend of the risk.

[0087] Among them, the risk index refers to a numerical indicator used to measure the degree of thermal risk of 1.5V lithium-ion batteries, which comprehensively considers factors such as temperature assessment value and strain trend value.

[0088] As an embodiment of the present invention, the calculating the risk index corresponding to the risk assessment level includes:

[0089] The risk index corresponding to the risk assessment level is calculated using the following formula:

[0090]

[0091] in, represents the risk index corresponding to the risk assessment level, represents the weight coefficient of the temperature assessment value corresponding to the risk assessment level, Indicates the number of temperature assessment values ​​corresponding to the risk assessment level, The index representing the temperature evaluation value, Indicates The battery surface temperature corresponding to the temperature evaluation value is Represents the average value of the battery surface temperature, Indicates the temperature range of the battery surface temperature, represents the weight coefficient of the strain trend value corresponding to the risk assessment level, represents the number of strain trend values, represents the index of the strain trend value, Indicates A quantitative indicator of the strain trend value, Represents the strain range of the strain eigenvalues.

[0092] In detail, the temperature evaluation value refers to the temperature obtained by monitoring and analyzing the battery surface temperature, such as the temperature value measured at different time points or different positions; the battery surface temperature refers to the actual temperature of the surface of a 1.5V lithium-ion battery, which can be obtained by real-time monitoring of equipment such as a temperature sensor; the temperature range refers to the fluctuation amplitude of the battery surface temperature; the strain trend value refers to the value related to the internal strain trend of the battery, which can reflect the physical changes of the battery under the action of heat; the quantitative index refers to the specific value obtained by quantifying the strain trend value, for example, it can be a specific index such as strain rate, strain amplitude, etc. that can reflect the strain trend; the strain range refers to the fluctuation amplitude of the strain trend value.

[0093] S3. Based on the risk index, analyze the battery status corresponding to the 1.5V lithium-ion battery, perform thermal load assessment on the battery status to obtain a thermal load assessment value, query the thermal impact factor corresponding to the assessment thermal load value, and mark the key impact area corresponding to the thermal impact factor.

[0094] The present invention analyzes the battery status corresponding to the 1.5V lithium-ion battery based on the risk index, and can promptly detect whether the battery is in an overheated, overcooled or other abnormal state, and can promptly take measures such as heat dissipation to prevent battery damage or dangerous situations. Secondly, it helps to optimize the battery use and management strategy.

[0095] Among them, the battery status refers to the comprehensive performance of various performance and physical and chemical characteristics of a 1.5V lithium-ion battery at a specific moment or time period, including but not limited to the battery's temperature status (whether it is overheated or overcooled), power status (remaining power percentage, charging status or discharging status), health status (such as battery aging, internal resistance changes, and other indicators reflecting battery performance degradation) and safety status (whether there are safety hazards such as thermal runaway risks and short circuit risks). Optionally, the analysis of the battery status corresponding to the 1.5V lithium-ion battery can be achieved through a battery management system, such as: the battery management system can collect battery temperature, voltage, current and other data in real time, and analyze and process these data through a built-in algorithm to evaluate the battery status.

[0096] Furthermore, the present invention obtains a heat load assessment value by performing a heat load assessment on the battery state, so as to clearly know the heat generated inside the battery due to chemical reactions, current flow and other factors, as well as the heat limit that the battery itself can withstand without affecting performance and safety, so as to take corresponding measures to avoid safety accidents caused by battery overheating, such as thermal runaway.

[0097] The thermal load assessment value refers to a quantitative value obtained through comprehensive analysis and calculation of multiple factors such as the battery's temperature distribution, heat flux density, and heat generation rate, and is used to indicate the degree of thermal load borne by the battery in its current state.

[0098] As an embodiment of the present invention, the thermal load assessment of the battery state to obtain a thermal load assessment value includes: analyzing the temperature distribution area corresponding to the battery state; determining the high temperature area and the low temperature area corresponding to the temperature distribution area; calculating the heat flux density corresponding to the battery state based on the high temperature area and the low temperature area; evaluating the heat generation rate corresponding to the battery state based on the heat flux density; and performing a thermal load assessment on the battery state based on the heat generation rate to obtain a thermal load assessment value.

[0099] Among them, the temperature distribution area refers to the range covered by the temperature distribution conditions at different positions on the surface or inside of a 1.5V lithium-ion battery; the high temperature area refers to the area where the relatively high temperature parts of the battery are located, and these areas may cause temperature increases due to concentrated chemical reactions inside the battery, poor heat dissipation or other reasons; the low temperature area refers to the area where the relatively low temperature parts of the battery are located, and the low temperature area may affect the chemical reaction rate and performance of the battery; the heat flux density refers to the amount of heat passing through a unit area per unit time, which describes the transfer of heat inside or on the surface of the battery; the heat generation rate refers to the rate at which the battery generates heat due to internal chemical reactions, resistance losses, etc. per unit time.

[0100] Further, the analysis of the temperature distribution area corresponding to the battery state can be implemented by a spatial interpolation algorithm, such as: Kriging interpolation method or inverse distance weighted interpolation method, interpolating the temperature data of discrete points to the entire battery space to obtain a continuous temperature distribution area; the determination of the high temperature area and the low temperature area corresponding to the temperature distribution area can be implemented by a threshold judgment method, such as: setting a high temperature threshold and a low temperature threshold, determining the part of the temperature distribution data that is higher than the high temperature threshold as the high temperature area, and the part that is lower than the low temperature threshold as the low temperature area; the calculation of the heat flux density corresponding to the battery state can be implemented by a heat flow meter, such as: a thin film heat flow meter can be attached to the battery surface to accurately measure the heat flow on the battery surface; the evaluation of the heat generation rate corresponding to the battery state can be implemented by a calorimetric method, such as: a calorimeter measures the heat exchange between the battery and the surrounding environment, and calculates the heat generation rate based on the heat change and the time interval; the thermal load evaluation of the battery state can be implemented by a comparative analysis method, such as: comparing the current maximum temperature of the battery with the maximum temperature threshold of the normal operation of the battery of this model, while considering the change of the heat generation rate, and comprehensively judging the thermal load condition.

[0101] By querying the thermal impact factor corresponding to the evaluated thermal load value, the present invention can clarify which factors contribute more to the thermal load of the battery, such as the battery's charge and discharge rate, ambient temperature, and the battery's internal structure. The battery's material selection, structural design, and thermal management system configuration can be adjusted to reduce the adverse effects of the thermal load on the battery, which is beneficial to improving the efficiency and accuracy of the battery's thermal management.

[0102] Among them, the thermal impact factor refers to various factors that affect the thermal load of the battery, including factors related to the battery's own characteristics (such as the internal resistance of the battery, the thermal stability of the electrode material, the capacity and charge and discharge rate of the battery, etc.), external environmental factors (such as ambient temperature, humidity, ventilation conditions, etc.) and factors related to the battery's working state (such as charge and discharge state, working current and voltage, etc.). Optionally, the query of the thermal impact factor corresponding to the evaluated thermal load value can be achieved through a control variable method, such as placing the battery at different ambient temperatures (controlled by a constant temperature box), while keeping other factors such as the battery's charge and discharge rate and internal resistance unchanged, measuring the thermal load evaluation values ​​at different ambient temperatures, thereby determining the impact of the thermal impact factor of ambient temperature on the thermal load.

[0103] Furthermore, by marking the key impact areas corresponding to the thermal impact factors, the present invention can intuitively understand which specific positions of the battery are more susceptible to the impact of the heat load, and can reasonably arrange the heat dissipation device or take insulation measures according to the key impact areas of different thermal impact factors to ensure that the temperature distribution of the battery is more uniform during operation and reduce the risk of local overheating.

[0104] The key impact area refers to the area that is most sensitive to the thermal impact factor and plays a key role in the change of battery thermal load, which is determined after the thermal sensitivity of the regional temperature data is evaluated.

[0105] As an embodiment of the present invention, marking the key impact area corresponding to the thermal impact factor includes: analyzing the impact degree value corresponding to the thermal impact factor; determining the potential impact area corresponding to the thermal impact factor based on the impact degree value; monitoring the regional temperature data corresponding to the potential impact area; evaluating the thermal sensitivity corresponding to the regional temperature data; and marking the key impact area corresponding to the thermal impact factor based on the thermal sensitivity.

[0106] Among them, the impact degree value refers to the quantitative value of the impact of the thermal impact factor on the battery thermal load, which reflects the closeness of the relationship between the thermal impact factor and the thermal load and its importance in causing the thermal load change; the potential impact area refers to the area inside or on the surface of the battery that is preliminarily inferred to be greatly affected by the thermal impact factor based on the nature and mode of action of the thermal impact factor and the structure and working principle of the battery; the regional temperature data refers to the set of temperature values ​​of the area at a specific time point or time period obtained by arranging temperature sensors in the potential impact area or using other temperature measurement methods; the thermal sensitivity refers to the sensitivity of the potential impact area to changes in the thermal impact factor.

[0107] Furthermore, the analysis of the influence degree value corresponding to the thermal influence factor can be achieved through a linear regression analysis method, such as: by fitting and training historical data to obtain a regression equation, the coefficient size of the independent variable in the equation can reflect the degree of its influence on the heat load, so as to determine the influence degree value; the determination of the potential influence area corresponding to the thermal influence factor can be achieved through finite element analysis tools, such as: ANSYS and other tools, based on the actual structure of the battery, material properties and the physical action mechanism corresponding to the thermal influence factor (such as heat conduction, heat convection and other principles) to establish a thermal model, simulate the heat transfer distribution in the battery under the action of the thermal influence factor, so as to determine Determine the potential impact area; the monitoring of the regional temperature data corresponding to the potential impact area can be achieved by a temperature sensor tool, such as: a thermistor temperature sensor, a thermocouple temperature sensor, etc.; the evaluation of the thermal sensitivity corresponding to the regional temperature data can be achieved by a sensitivity analysis algorithm, such as: a local sensitivity analysis, a global sensitivity analysis and other algorithms; the marking of the key impact area corresponding to the thermal impact factor can be achieved by a clustering analysis method, such as: using the K-Means clustering method, grouping areas with similar thermal sensitivity into one category, and then selecting the area included in the category with higher thermal sensitivity from the clustering results, and marking it as the key impact area.

[0108] S4. Based on the key impact area, determine the thermal management path corresponding to the 1.5V lithium-ion battery, analyze the path transfer amount corresponding to the thermal management path, calculate the heat transfer efficiency corresponding to the thermal management path based on the path transfer amount, optimize the operating parameters corresponding to the 1.5V lithium-ion battery based on the heat transfer efficiency, and calculate the heat drop ratio corresponding to the operating parameters.

[0109] The present invention determines the thermal management path corresponding to the 1.5V lithium-ion battery based on the key impact area, analyzes the path transfer amount corresponding to the thermal management path, and can plan the thermal management path in a targeted manner to ensure that heat can be effectively dissipated from these key parts that are prone to thermal problems or imported when needed, thereby making the temperature distribution of the battery more uniform, minimizing local overheating, and ensuring stable battery performance.

[0110] The thermal management path refers to the route involved in a series of heat transfer modes such as heat conduction, convection or radiation, starting from the key impact area in order to effectively control the battery temperature in the 1.5V lithium-ion battery system. These routes can be channels formed by the internal structural components of the battery (such as electrodes, diaphragms, electrolytes), external heat dissipation devices (such as heat sinks, heat pipes, cooling channels) and the surrounding environment; the path transfer amount refers to the amount of heat passing through a specific cross-section per unit time in the thermal management path, which reflects the heat transmission capacity and flow rate in the management path, and its size is affected by the thermal conductivity of the material of the thermal management path, the geometric shape of the path (such as cross-sectional area, length), temperature difference and heat transfer mode (conduction, convection, radiation) and other factors. Optionally, the determination of the thermal management path corresponding to the 1.5V lithium-ion battery can be achieved by thermal analysis tools, such as: ANSYS In Icepak, the temperature distribution and heat flow path of the battery under different working conditions can be simulated; the analysis of the path transfer amount corresponding to the thermal management path can be achieved through a finite element analysis algorithm, such as: using tools such as ANSYS or COMSOL to perform finite element analysis, simulate the heat conduction process of the battery heat sink, and calculate the heat transfer amount through the heat sink as the path transfer amount of the thermal management path.

[0111] Furthermore, the present invention calculates the heat transfer efficiency corresponding to the thermal management path based on the path transfer amount, which can quantitatively understand the performance of the thermal management system in actual work and determine whether it can meet the thermal regulation requirements required for normal operation of the battery.

[0112] The heat transfer efficiency refers to an indicator that measures the effectiveness of a heat management path in the process of transferring heat.

[0113] As an embodiment of the present invention, the step of calculating the heat transfer efficiency corresponding to the thermal management path based on the path transfer amount includes:

[0114] The heat transfer efficiency corresponding to the thermal management path is calculated using the following formula:

[0115]

[0116] in, represents the heat transfer efficiency corresponding to the thermal management path, represents the thermal output power corresponding to the thermal management path, represents the number of path nodes corresponding to the thermal management path, Indicates the node index corresponding to the path node, Indicates The transfer heat corresponding to each path node is: Indicates The node temperature corresponding to each path node is represents the heat flux density function corresponding to the thermal management path, Indicates the time threshold corresponding to the thermal management path.

[0117] In detail, the thermal output power refers to the heat output by the thermal management path per unit time, which reflects the ability of the thermal management system to remove heat from the battery; the path node refers to a specific position or key point on the thermal management path, such as different areas on the battery surface, specific points on the heat sink, etc.; the transferred heat refers to the heat actually transferred at the path node on the thermal management path; the node temperature refers to the temperature at the path node, and the high or low node temperature directly affects the direction and rate of heat transfer; the heat flux density function refers to a function that describes the change of heat flux density on the thermal management path with time, the heat passing through a unit area per unit time, which reflects the intensity of heat transfer; the time threshold refers to a time limit set when calculating the heat transfer efficiency, which represents the heat transfer situation of the thermal management path within a certain time range, such as the battery's charge and discharge cycle, the equipment's working time, etc.

[0118] Furthermore, the present invention optimizes the operating parameters corresponding to the 1.5V lithium-ion battery based on the heat transfer efficiency, which can significantly improve the performance of the battery, and by adjusting the operating parameters such as the charge and discharge rate, operating current and voltage according to the heat transfer efficiency, it can ensure that the heat generated by the battery during operation can be effectively dissipated or managed, thereby avoiding excessive internal temperature of the battery due to heat accumulation.

[0119] Among them, the operating parameters refer to various adjustable parameters that can affect the performance, efficiency, safety and thermal characteristics of the 1.5V lithium-ion battery during its operation, such as: charging and discharging parameters, temperature parameters, time parameters, power parameters, etc. Optionally, the optimization of the operating parameters corresponding to the 1.5V lithium-ion battery can be achieved through a battery testing system, such as: Arbin battery testing system, Neware battery testing system, etc.

[0120] Furthermore, the present invention calculates the heat drop ratio corresponding to the operating parameters, which helps to adjust the operating parameters in real time according to the thermal state of the battery in actual applications, so as to achieve more effective thermal control, prevent the battery from being affected by overheating and affecting performance and life, and ensure safe and stable operation of the battery.

[0121] The heat drop ratio refers to an indicator that measures the degree of heat reduction in the system under specific operating parameters, and reflects the thermal change of the system from the initial state to the current state after adjusting the operating parameters.

[0122] As an embodiment of the present invention, the calculating the heat drop ratio corresponding to the operating parameter includes:

[0123] The heat drop ratio corresponding to the operating parameters is calculated using the following formula:

[0124]

[0125] in, represents the heat drop ratio corresponding to the operating parameters, represents the number of monitoring points corresponding to the operating parameters, Indicates the number index of monitoring points, represents the average operating temperature corresponding to the operating parameters, Indicates The temperature value of each monitoring point, Indicates The heat flow at each monitoring point, Indicates The thermal resistance value of each monitoring point is represents the number of power points corresponding to the operating parameters, Indicates the number index of power points, Indicates The power output value corresponding to each power point is: Indicates The state time corresponding to each power point.

[0126] In detail, the monitoring point refers to a position set in the thermal management system for measuring specific physical quantities; the operating average temperature refers to the average value of the temperatures of all monitoring points during the operation of the thermal management system, which reflects the overall temperature level of the system and can be used as a reference value to evaluate the thermal state of the system; the heat flow refers to the amount of heat passing through a unit area per unit time at a monitoring point, and the magnitude of the heat flow depends on factors such as the temperature difference and thermal conductivity coefficient at the monitoring point; the thermal resistance value refers to the degree of obstruction to heat flow at the monitoring point. The larger the thermal resistance value, the greater the obstruction to heat flow at this position and the more difficult the heat transfer is; the power point refers to a specific position or state related to power output in the thermal management system; the power output value refers to the power output at the power point; the state time refers to the length of time that the power point is in a specific state. The state time can reflect the duration of the power point in different states, which has an important impact on heat accumulation and heat dissipation in thermal management.

[0127] S5. Based on the heat drop ratio, extract the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery; based on the multi-thermal energy characteristics, query the hotspot distribution state inside the 1.5V lithium-ion battery; analyze the hotspot tags corresponding to the hotspot distribution state; based on the hotspot tags, generate a thermal management solution corresponding to the 1.5V lithium-ion battery.

[0128] Based on the heat drop ratio, the present invention extracts the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery, which can reveal the inherent laws of heat generation, transfer and dissipation of the battery under various operating conditions, and can perform targeted optimization in battery material selection and structural design to reduce heat generation or improve heat dissipation efficiency.

[0129] Among them, the multi-thermal energy characteristics refer to a series of feature sets extracted from the thermal behavior and thermal characteristics of the battery that can comprehensively reflect the characteristics of the battery in terms of thermal energy generation, transfer, storage and dissipation under different conditions. These characteristics may include but are not limited to heat capacity, thermal conductivity, thermal diffusion coefficient, heat generation rate, etc. at different temperatures.

[0130] As an embodiment of the present invention, the extracting of multiple thermal energy characteristics corresponding to the 1.5V lithium-ion battery based on the heat drop ratio includes: analyzing the correlation between the heat drop ratio and the battery temperature corresponding to the 1.5V lithium-ion battery; based on the correlation, screening the temperature interval data corresponding to the 1.5V lithium-ion battery; extracting the temperature flow characteristics in the temperature interval data; determining the heat conduction mode corresponding to the 1.5V lithium-ion battery according to the temperature flow characteristics; and extracting the multiple thermal energy characteristics corresponding to the 1.5V lithium-ion battery based on the heat conduction mode.

[0131] Among them, the correlation relationship refers to the quantitative or qualitative relationship of mutual influence and mutual restriction between the heat drop ratio and the temperature of the 1.5V lithium-ion battery, which describes how the heat drop ratio changes accordingly with the change of the battery temperature, and the influence of different heat drop ratios on the battery temperature distribution and change trend; the temperature range data refers to the battery temperature range data with specific significance or showing specific thermal behavior screened out after analyzing the correlation relationship between the heat drop ratio and the battery temperature; the temperature flow characteristics refer to the relevant parameters or characteristics reflecting the heat transfer and flow characteristics inside the battery within the specific temperature range data, which may include information such as the rate of temperature change, the distribution of temperature gradients, and the direction of heat transfer; the heat conduction mode refers to the way and path of heat transfer inside the battery. Common heat conduction modes include heat conduction (heat transfer through the solid part of the battery material), thermal convection (heat transfer through the fluid medium around the battery) and thermal radiation (heat transfer in the form of electromagnetic waves).

[0132] Furthermore, the analysis of the correlation between the heat drop ratio and the battery temperature corresponding to the 1.5V lithium-ion battery can be achieved through data acquisition equipment, such as: temperature sensor, data recorder, etc.; the screening of the temperature range data corresponding to the 1.5V lithium-ion battery can be achieved through a clustering algorithm, such as: K-Means, EM algorithm, etc.; the extraction of temperature flow characteristics in the temperature range data can be achieved through a feature extraction algorithm, such as: PCA, LDA and other algorithms; the determination of the heat conduction mode corresponding to the 1.5V lithium-ion battery can be achieved through a temperature distribution measurement method, such as: using multiple temperature sensors (such as thermocouples, thermistors) arranged at different positions inside the battery (such as the positive electrode, the negative electrode, near the diaphragm, etc.) and on the surface, so as to determine the heat conduction mode corresponding to the battery; the extraction of the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery can be achieved through numerical simulation tools, such as: ANSYS, COMSOL and other tools.

[0133] Based on the multi-thermal energy characteristics, the present invention queries the hot spot distribution state inside the 1.5V lithium-ion battery and analyzes the hot spot labels corresponding to the hot spot distribution state, which can more clearly understand the areas where heat is concentrated and accumulated during the operation of the battery, thereby providing a strong basis for optimizing battery design and material selection.

[0134] Among them, the hot spot distribution state refers to the position distribution of areas with high heat concentration in the battery space inside the 1.5V lithium-ion battery, as well as a comprehensive description of the temperature values, temperature gradients and other related thermal characteristics of these hot spot areas; the hot spot label refers to a feature or attribute used to classify, identify and describe the hot spot distribution state. The hot spot label may include the severity of the hot spot (such as high, medium and low level hot spots), the cause of the hot spot (for example, hot spots caused by excessive charging and discharging rates, hot spots caused by internal short circuits in the battery, etc.), the battery area where the hot spot is located (such as hot spots near the positive electrode, hot spots at the diaphragm, etc.), and the type of impact of the hot spot on battery performance (such as hot spots that affect battery capacity, hot spots that may cause thermal runaway risks, etc.). Optionally, the query of the hot spot distribution state inside the 1.5V lithium-ion battery can be achieved by an infrared thermal imager, such as FLIR, Testo, etc.; the analysis of the hot spot label corresponding to the hot spot distribution state can be achieved by a Python data analysis library, such as NumPy, Pandas, Scikit-learn, etc.

[0135] Furthermore, the present invention generates a thermal management solution corresponding to the 1.5V lithium-ion battery based on the hotspot label, wherein the hotspot label clearly identifies the location and characteristics of heat concentration inside the battery, so that the thermal management solution can accurately focus on these key areas, thereby ensuring that when the battery is operating at high power, the temperature of the hotspot area can be timely and effectively controlled to avoid damage to battery performance and life due to overheating.

[0136] Among them, the thermal management solution refers to a collection of comprehensive measures and strategies formulated for the heat generated by the 1.5V lithium-ion battery during operation and the distribution of hot spots to ensure that the battery can operate stably, safely and efficiently within an appropriate temperature range, including heat dissipation measures, heating strategies, temperature monitoring and control systems, etc. Optionally, the generation of the thermal management solution corresponding to the 1.5V lithium-ion battery can be achieved through a solution generation tool, such as: FloTHERM, ICEPAK and other tools.

[0137] First, the present invention obtains a 1.5V lithium-ion battery and monitors the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, so as to timely grasp the working status of the battery and avoid adverse effects on battery performance and life due to excessively high or low temperature. In addition, understanding the trend of battery surface temperature changes can provide an important basis for optimizing battery use and management. At the same time, the present invention helps to predict in advance possible overheating or overcooling of the battery by analyzing the strain trend corresponding to the key sequence value, and provides a basis for subsequent thermal management measures to ensure that the battery operates in a safe and efficient state. Based on the risk index, the present invention analyzes the battery status corresponding to the 1.5V lithium-ion battery, and can timely discover whether the battery is in an overheated, overcooled or other abnormal state, and can timely take measures such as heat dissipation to prevent battery damage or danger. Secondly, it helps to optimize the use and management strategy of the battery. The present invention determines the thermal management path corresponding to the 1.5V lithium-ion battery based on the key impact area, analyzes the path transfer amount corresponding to the thermal management path, and can plan the thermal management path in a targeted manner to ensure that heat can be effectively dissipated from these key parts that are prone to thermal problems or imported when needed, so that the temperature distribution of the battery is more uniform, minimizes local overheating, and ensures the stable performance of the battery. Furthermore, based on the heat drop ratio, the present invention extracts the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery, which can reveal the inherent laws of heat generation, transfer and dissipation of the battery under various operating conditions, and can be optimized in a targeted manner during battery material selection and structural design to reduce heat generation or improve heat dissipation efficiency. Therefore, the 1.5V lithium-ion battery thermal management method and system based on temperature strain analysis proposed in the present invention can improve the thermal management level of 1.5V lithium-ion batteries.

[0138] Embodiment 2:

[0139] like Figure 2 , which is a module schematic diagram of a 1.5V lithium-ion battery thermal management system based on temperature strain analysis provided by an embodiment of the present invention.

[0140] The 1.5V lithium-ion battery thermal management system 200 based on temperature strain analysis described in the present invention can be installed in an electronic device. According to the functions implemented, the 1.5V lithium-ion battery thermal management system 200 based on temperature strain analysis can include a sequence value extraction module 201, an index calculation module 202, an area marking module 203, a heat drop ratio calculation module 204 and a solution generation module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0141] In this embodiment, the functions of each module / unit are as follows:

[0142] The sequence value extraction module 201 is used to obtain a 1.5V lithium-ion battery, monitor the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, collect the temperature strain data sequence corresponding to the battery surface temperature, and extract the key sequence value in the temperature strain data sequence;

[0143] An indicator calculation module 202 is used to analyze the strain trend corresponding to the key sequence value, perform risk assessment on the strain trend to obtain risk assessment data, classify the risk assessment data to obtain a risk assessment level, and calculate a risk index corresponding to the risk assessment level;

[0144] The area marking module 203 is used to analyze the battery state corresponding to the 1.5V lithium-ion battery based on the risk index, perform a heat load assessment on the battery state to obtain a heat load assessment value, query a heat impact factor corresponding to the assessment heat load value, and mark a key impact area corresponding to the heat impact factor;

[0145] A heat drop ratio calculation module 204 is used to determine a thermal management path corresponding to the 1.5V lithium-ion battery based on the key impact area, analyze a path transfer amount corresponding to the thermal management path, calculate a heat transfer efficiency corresponding to the thermal management path based on the path transfer amount, optimize an operating parameter corresponding to the 1.5V lithium-ion battery based on the heat transfer efficiency, and calculate a heat drop ratio corresponding to the operating parameter;

[0146] The solution generation module 205 is used to extract the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery based on the heat drop ratio, query the hot spot distribution state inside the 1.5V lithium-ion battery based on the multi-thermal energy characteristics, analyze the hot spot tags corresponding to the hot spot distribution state, and generate a thermal management solution corresponding to the 1.5V lithium-ion battery based on the hot spot tags.

[0147] In detail, the modules described in the 1.5V lithium-ion battery thermal management system 200 based on temperature strain analysis described in the embodiment of the present invention adopt the same technical means as the 1.5V lithium-ion battery thermal management method based on temperature strain analysis described in the accompanying drawings when in use, and can produce the same technical effects, which will not be repeated here.

[0148] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis, characterized in that: The method comprises: Obtain a 1.5V lithium-ion battery, monitor the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, collect a temperature strain data sequence corresponding to the battery surface temperature, and extract a key sequence value in the temperature strain data sequence; Analyze the strain trend corresponding to the key sequence value, perform risk assessment on the strain trend to obtain risk assessment data, classify the risk assessment data to obtain a risk assessment level, and calculate the risk index corresponding to the risk assessment level; Based on the risk index, analyzing the battery state corresponding to the 1.5V lithium-ion battery, performing a thermal load assessment on the battery state to obtain a thermal load assessment value, querying a thermal impact factor corresponding to the thermal load assessment value, and marking a key impact area corresponding to the thermal impact factor; Based on the key impact area, determining a thermal management path corresponding to the 1.5V lithium-ion battery, analyzing a path transfer amount corresponding to the thermal management path, calculating a heat transfer efficiency corresponding to the thermal management path based on the path transfer amount, optimizing operating parameters corresponding to the 1.5V lithium-ion battery based on the heat transfer efficiency, and calculating a heat drop ratio corresponding to the operating parameters; Based on the heat drop ratio, the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery are extracted. Based on the multi-thermal energy characteristics, the hotspot distribution state inside the 1.5V lithium-ion battery is queried, the hotspot tags corresponding to the hotspot distribution state are analyzed, and based on the hotspot tags, a thermal management solution corresponding to the 1.5V lithium-ion battery is generated.

2. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in claim 1, characterized in that: The extracting of the key sequence value in the temperature strain data sequence includes: Segmenting the temperature strain data sequence to obtain segmented subsequences; identifying sequence fluctuation segments in the segmented subsequences; Performing peak detection on the data in the fluctuation segment of the sequence to obtain a peak point set; Analyze the time intervals corresponding to the peak points in the peak point set; Based on the time interval, a key sequence value in the temperature strain data sequence is extracted.

3. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in claim 1, characterized in that: The risk assessment of the strain trend to obtain risk assessment data includes: analyzing a strain rate corresponding to the strain trend; Based on the strain rate, dividing the risk level range corresponding to the strain trend; Query the risk characteristic indicators of different levels corresponding to the risk level range; Based on the risk characteristic index, a risk assessment is performed on the strain trend to obtain risk assessment data.

4. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in claim 1, characterized in that: The calculating the risk index corresponding to the risk assessment level includes: The risk index corresponding to the risk assessment level is calculated using the following formula: Wherein, RI represents the risk index corresponding to the risk assessment level, α represents the weight coefficient of the temperature assessment value corresponding to the risk assessment level, n represents the number of temperature assessment values ​​corresponding to the risk assessment level, i represents the index of the temperature assessment value, T i represents the battery surface temperature corresponding to the ith temperature evaluation value, T avg Represents the average battery surface temperature, T range represents the temperature range of the battery surface temperature, β represents the weight coefficient of the strain trend value corresponding to the risk assessment level, m represents the number of strain trend values, j represents the index of the strain trend value, S j Represents the quantitative index of the j-th strain trend value, S range Represents the strain range of the strain eigenvalues.

5. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in claim 1, characterized in that: The step of performing thermal load evaluation on the battery state to obtain a thermal load evaluation value includes: Analyzing the temperature distribution area corresponding to the battery state; Determine a high temperature area and a low temperature area corresponding to the temperature distribution area; Calculating a heat flux density corresponding to the battery state based on the high temperature region and the low temperature region; According to the heat flux density, evaluating a heat generation rate corresponding to the battery state; Based on the heat generation rate, a heat load evaluation is performed on the battery state to obtain a heat load evaluation value.

6. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in claim 1, characterized in that: The key impact area corresponding to the heat impact factor is marked, including: Analyze the impact degree value corresponding to the thermal impact factor; Based on the impact degree value, determining the potential impact area corresponding to the thermal impact factor; Monitor regional temperature data corresponding to the potential impact area; Evaluating thermal sensitivity corresponding to the regional temperature data; Based on the thermal sensitivity, a key impact area corresponding to the thermal impact factor is marked.

7. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in claim 1, characterized in that: The calculating the heat transfer efficiency corresponding to the thermal management path based on the path transfer amount includes: The heat transfer efficiency corresponding to the thermal management path is calculated using the following formula: Wherein, η represents the heat transfer efficiency corresponding to the thermal management path, P represents the heat output power corresponding to the thermal management path, n′ represents the number of path nodes corresponding to the thermal management path, i′ represents the node index corresponding to the path node, Q i′ represents the transfer heat corresponding to the i′th path node, TW i′ represents the node temperature corresponding to the i′th path node, H(t) represents the heat flux density function corresponding to the thermal management path, and T represents the time threshold corresponding to the thermal management path.

8. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in claim 1, characterized in that: The calculating the heat drop ratio corresponding to the operating parameter includes: The heat drop ratio corresponding to the operating parameters is calculated using the following formula: Wherein, Rb represents the heat drop ratio corresponding to the operating parameter, m′ represents the number of monitoring points corresponding to the operating parameter, j′ represents the number index of the monitoring points, and R avg represents the average operating temperature corresponding to the operating parameters, R j′ represents the temperature value of the j′th monitoring point, Q j′ represents the heat flux at the j′th monitoring point, θ j′ represents the thermal resistance value of the j′th monitoring point, p represents the number of power points corresponding to the operating parameter, k represents the number index of the power points, G P represents the power output value corresponding to the pth power point, Y P Indicates the state time corresponding to the pth power point.

9. A 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in claim 1, characterized in that: The step of extracting the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery based on the heat drop ratio includes: Analyzing the correlation between the heat drop ratio and the battery temperature corresponding to the 1.5V lithium-ion battery; Based on the association relationship, the temperature range data corresponding to the 1.5V lithium-ion battery is screened; extracting temperature flow characteristics from the temperature interval data; Determining a heat conduction mode corresponding to the 1.5V lithium-ion battery according to the temperature flow characteristics; Based on the heat conduction mode, multiple thermal energy characteristics corresponding to the 1.5V lithium-ion battery are extracted.

10. A 1.5V lithium-ion battery thermal management system based on temperature strain analysis, characterized in that: A system for executing a 1.5V lithium-ion battery thermal management method based on temperature strain analysis as claimed in any one of claims 1 to 9, the system comprising: A sequence value extraction module is used to obtain a 1.5V lithium-ion battery, monitor the battery surface temperature corresponding to the 1.5V lithium-ion battery in real time, collect a temperature strain data sequence corresponding to the battery surface temperature, and extract a key sequence value from the temperature strain data sequence; An indicator calculation module is used to analyze the strain trend corresponding to the key sequence value, perform risk assessment on the strain trend to obtain risk assessment data, classify the risk assessment data to obtain a risk assessment level, and calculate a risk index corresponding to the risk assessment level; A region marking module, for analyzing the battery state corresponding to the 1.5V lithium-ion battery based on the risk index, performing a heat load assessment on the battery state to obtain a heat load assessment value, querying a heat impact factor corresponding to the heat load assessment value, and marking a key impact region corresponding to the heat impact factor; a heat drop ratio calculation module, for determining a thermal management path corresponding to the 1.5V lithium-ion battery based on the key impact area, analyzing a path transfer amount corresponding to the thermal management path, calculating a heat transfer efficiency corresponding to the thermal management path based on the path transfer amount, optimizing an operating parameter corresponding to the 1.5V lithium-ion battery based on the heat transfer efficiency, and calculating a heat drop ratio corresponding to the operating parameter; A solution generation module is used to extract the multi-thermal energy characteristics corresponding to the 1.5V lithium-ion battery based on the heat drop ratio, query the hot spot distribution state inside the 1.5V lithium-ion battery based on the multi-thermal energy characteristics, analyze the hot spot tags corresponding to the hot spot distribution state, and generate a thermal management solution corresponding to the 1.5V lithium-ion battery based on the hot spot tags.

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