CN117134007B

CN117134007B - Temperature control method and system for lithium ion battery

Info

Publication number: CN117134007B
Application number: CN202311368893.0A
Authority: CN
Inventors: 林彬
Original assignee: Shenzhen Shentong World Technology Co ltd
Current assignee: Shenzhen Shentong World Technology Co ltd
Priority date: 2023-10-23
Filing date: 2023-10-23
Publication date: 2024-01-26
Anticipated expiration: 2043-10-23
Also published as: CN117134007A

Abstract

The invention relates to the technical field of lithium batteries, in particular to a temperature control method and a temperature control system for a lithium ion battery. The method comprises the following steps: acquiring a lithium battery multi-physical-field image set and lithium battery working condition data, and detecting abnormal heat energy to acquire a classified heat energy image set; constructing a three-dimensional temperature distribution map according to the classified heat energy image set and simulating to obtain three-dimensional temperature field simulation data; calculating a temperature controllable range of the lithium battery working condition data and the three-dimensional temperature field simulation data to obtain the temperature controllable range of the lithium battery; constructing a lithium battery temperature prediction model according to the three-dimensional temperature field simulation data; acquiring a real-time lithium battery thermal energy image, and predicting the real-time lithium battery thermal energy image and lithium battery working condition data to acquire working temperature prediction data; and calculating the working temperature prediction data to obtain thermal runaway adjustment data and correcting the thermal runaway adjustment data so as to obtain lithium battery temperature adjustment data. The invention controls the temperature of the lithium ion battery based on data mining.

Description

Temperature control method and system for lithium ion battery

Technical Field

The invention relates to the technical field of lithium batteries, in particular to a temperature control method and a temperature control system for a lithium ion battery.

Background

Lithium ion batteries are one of the most common battery types in current electronic devices and automobiles. However, control of the battery temperature becomes a critical issue during charge and discharge. Improper temperature management can lead to overheating of the battery, compromising its performance and even causing safety concerns. Existing temperature control methods typically rely on temperature sensors and temperature regulation devices. However, these methods may have problems such as inefficiency and slower response speed.

Disclosure of Invention

Accordingly, the present invention is directed to a temperature control method for a lithium ion battery, which solves at least one of the above-mentioned problems.

In order to achieve the above object, a temperature control method for a lithium ion battery comprises the following steps:

step S1: acquiring a lithium battery multi-physical-field image set and lithium battery working condition data through a preset sensor, and detecting abnormal heat energy of the lithium battery multi-physical-field image set so as to obtain a classified heat energy image set;

step S2: constructing a three-dimensional temperature distribution map according to the classified heat energy image set, and performing multi-physical field coupling simulation on the three-dimensional temperature distribution map and the lithium battery multi-physical field image set so as to obtain three-dimensional temperature field simulation data;

Step S3: calculating the temperature controllable range of the lithium battery working condition data and the three-dimensional temperature field simulation data, thereby obtaining the temperature controllable range of the lithium battery;

step S4: constructing a lithium battery temperature prediction model according to the three-dimensional temperature field simulation data;

step S5: acquiring a real-time lithium battery thermal energy image, and predicting the battery working temperature by utilizing a lithium battery temperature prediction model to the real-time lithium battery thermal energy image and the lithium battery working condition data, so as to acquire working temperature prediction data;

step S6: and performing thermal runaway minimization calculation on the working temperature prediction data according to the lithium battery temperature controllable range so as to obtain thermal runaway adjustment data, and performing adaptive correction on the lithium battery working condition data according to the thermal runaway adjustment data so as to obtain the lithium battery temperature adjustment data.

According to the invention, the preset sensor is used for acquiring the multi-physical-field image and the working condition data, so that detailed understanding of the current state of the lithium battery can be provided. Abnormal thermal energy detection helps to identify early problems that may cause overheating of the battery, thereby preventing degradation of battery performance or safety issues. At the same time, this means that more complex control measures are only activated when needed, thus increasing the energy utilization. The construction of three-dimensional temperature profiles and multi-physical field coupling simulation can help simulate the complexity of the internal temperature distribution of the battery. This helps to better understand the dynamic changes in the internal temperature of the battery, thereby providing more accurate temperature data for control and prediction. Calculation of the temperature controllable range allows determination of a safe controllable temperature range during battery operation. This helps to prevent overheating or overcooling of the battery, thereby extending battery life and improving safety. The temperature prediction model is constructed based on three-dimensional temperature field simulation data and can be used for predicting the future temperature change of the battery. This helps take measures in advance to prevent overheating or overcooling of the battery, thereby improving battery performance and safety. The temperature prediction model can predict the change in the battery temperature in advance so that control measures can be taken more quickly to cope with the temperature change. This increases the response speed of the system and helps to prevent the temperature from rising to dangerous levels. The lithium battery temperature prediction model is used for predicting the working temperature of the battery according to the real-time thermal energy image and the working condition data. This helps to monitor the battery condition in real time and take control measures to ensure that the battery is operating within a safe temperature range. Through the thermal runaway minimization calculation, the adjustment measures that need to be taken can be determined to ensure that the battery temperature is always within a controllable range. The adaptability correction further improves the control accuracy, so that the battery can maintain good temperature control under different working conditions.

Optionally, step S1 specifically includes:

step S11: acquiring a lithium battery multi-physical-field image set and lithium battery working condition data through a preset sensor;

step S12: extracting a thermal energy image set from the lithium battery multi-physical field image set, thereby obtaining a lithium battery thermal energy image set;

step S13: extracting an idle thermal energy image of the lithium battery and an operating thermal energy image of the lithium battery from the thermal energy image set of the lithium battery, so as to obtain the idle thermal energy image set and the operating thermal energy image set;

step S14: detecting the idle abnormal heat energy of the idle heat energy image set, so as to obtain an idle abnormal heat energy image and an idle normal heat energy image;

step S15: detecting abnormal heat energy of the work heat energy image set, so as to obtain an abnormal heat energy image and a normal heat energy image;

step S16: and carrying out time sequence combination on the idle abnormal heat energy image, the idle normal heat energy image, the working abnormal heat energy image and the working normal heat energy image, thereby obtaining a classified heat energy image set.

According to the invention, the multi-physical-field image and the working condition data of the lithium battery are obtained by using the preset sensor, so that detailed battery state information can be provided for the follow-up. This includes important parameters such as temperature, voltage, current, etc. inside the battery, which help to monitor the operating state of the battery in real time. By extracting the thermal energy image set from the multi-physical field image set, the focus can be placed on the thermal energy distribution of the battery. This helps to more accurately understand the heat conduction and temperature distribution inside the battery, and provides necessary data for subsequent thermal anomaly detection. The separation of the thermal energy image set into idle and operating thermal energy images helps to distinguish the thermal characteristics of the battery under different operating conditions. This enables a more accurate monitoring of the operating conditions of the battery, both at rest and at work. Detecting the idle abnormal thermal energy image helps to identify early anomalies in the battery when not in use, such as spontaneous heating or thermal anomalies due to failure. This helps to prevent the battery from suffering unnecessary heat loss or potential safety problems during idle conditions. Detecting an abnormal thermal energy image of operation helps to identify early anomalies in the battery during use, such as thermal anomalies in charge or discharge. This helps to prevent overheating or other performance problems of the battery and improves the safety and reliability of the battery. And the abnormal thermal energy images and the normal thermal energy images in the idle state and the working state are combined in a time sequence, so that the comprehensive analysis of the thermal energy characteristics of the battery is facilitated. This provides a set of classified thermal energy images for subsequent use in constructing temperature prediction models and temperature control strategies.

Optionally, step S13 specifically includes:

step S131: performing time sequence extraction on the idle thermal energy image set to obtain idle time sequence data, and performing comparison analysis on the idle time sequence data to obtain long idle time sequence data and short idle time sequence data;

step S132: extracting a local temperature extremum from the idle thermal energy image set according to a preset time proportion, so as to obtain the local temperature extremum;

step S133: carrying out temperature gradient calculation by utilizing the local temperature extreme value and idle time sequence data, thereby obtaining local temperature gradient data;

step S134: carrying out statistical analysis on the local temperature gradient data so as to obtain high-frequency temperature gradient data and low-frequency temperature gradient data;

step S135: performing intersection data screening on the idle thermal energy image set according to the short idle time sequence data and the high-frequency temperature gradient data, so as to obtain an idle normal thermal energy image;

step S136: and screening intersection data of the idle heat energy image set according to the long idle time sequence data and the low-frequency temperature gradient data, so as to obtain an idle abnormal heat energy image.

The invention can monitor the temperature change trend of the battery when not in use by extracting the idle time sequence data. This helps to identify the temperature characteristics of the battery during different idle periods, thereby distinguishing between long-term and short-term idle conditions. The extraction of the local temperature extremum may capture peaks and valleys of the internal temperature distribution of the battery. This helps to understand the temperature gradient inside the battery, identify possible local temperature anomalies, and analyze temperature fluctuations. Calculating the local temperature gradient allows quantifying the rate of change of the temperature. This helps identify the temperature gradient, i.e., the spatial variation of temperature, inside the battery, thereby helping to detect potential thermal anomalies. Statistical analysis of the temperature gradient data can identify the temperature gradient components at high and low frequencies. The high frequency temperature gradient may be associated with rapid changes inside the battery, while the low frequency temperature gradient may reflect temperature changes over a longer time scale. This helps refine the sensitivity of anomaly detection. By screening the idle thermal images according to the short idle time sequence data and the high-frequency temperature gradient data, the battery state with small and normal temperature fluctuation in a short time can be identified. This helps to reduce false alarm anomalies and improve the accuracy of anomaly detection. By screening the idle thermal images according to the long idle time sequence data and the low-frequency temperature gradient data, the battery which is in an idle state for a long time and has abnormal temperature gradient can be identified. This helps to detect long term anomalies in the battery early to prevent potential safety issues.

Optionally, step S14 specifically includes:

step S141: classifying the working image sets according to working condition data of the lithium battery, so as to obtain a charging heat energy image set and a discharging heat energy image set;

step S142: performing frequency spectrum anomaly detection on the charging heat energy image set so as to obtain a charging normal heat energy image and a charging abnormal heat energy image;

step S143: constructing an abnormal heat energy classification model according to the charging normal heat energy image and the charging abnormal heat energy image;

step S144: counting the high-frequency heat energy components of the discharge heat energy image set, thereby obtaining high-frequency heat energy components;

step S145: calculating a component temperature peak value of the discharge heat energy image set, thereby obtaining a component temperature peak value;

step S146: carrying out abnormal temperature peak value extraction on the component temperature peak value according to a preset temperature peak value threshold value, so as to obtain an abnormal component temperature peak value;

step S147: constructing an abnormal discharge thermal energy portrait according to the abnormal component temperature peak value and the high-frequency thermal energy component;

step S148: classifying and calculating the discharge heat energy image set by using the abnormal heat energy classification model and using the abnormal discharge heat energy image, thereby obtaining a discharge normal heat energy image and a discharge abnormal heat energy image;

Step S149: the charging normal heat energy image and the discharging normal heat energy image are combined in time sequence, so that a working normal heat energy image is obtained; and carrying out time sequence combination on the charge abnormal heat energy image and the discharge abnormal heat energy image, thereby obtaining the work abnormal heat energy image.

The invention classifies the working thermal energy image set into the charging thermal energy image set and the discharging thermal energy image set, which is helpful for distinguishing the thermal energy characteristics of the battery under different working states. This can help monitor the temperature change of the battery during charging and discharging, providing the basis data for anomaly detection. By detecting the frequency spectrum abnormality of the charging thermal energy image, the abnormal thermal energy condition occurring during charging can be identified. This helps to find problems in the charging process in advance to prevent overheating or other abnormal conditions of the battery. The abnormal thermal energy classification model can be constructed to classify the charged thermal energy image into normal and abnormal conditions according to the characteristics of the charged thermal energy image. This facilitates automated anomaly detection and can be used to monitor the operating condition of the battery in real time. The high-frequency temperature change condition inside the battery can be identified by statistically analyzing the high-frequency thermal energy component of the discharge thermal energy image. This helps detect anomalies such as hot spots or temperature spikes for a short period of time. Calculating the temperature peaks of the components in the discharge thermal image helps to understand the extreme conditions of the internal temperature of the battery. This can be used to detect potential overheating problems or abnormal temperature peak conditions. Extracting abnormal component temperature peaks may help identify anomalies that may exist during the discharge process. By setting a preset temperature peak value threshold, abnormal temperature peaks can be screened out for subsequent analysis. Constructing an abnormal discharge thermal energy representation from the abnormal component temperature peaks and the high frequency thermal energy components is helpful for visualizing and understanding the abnormal condition of the battery when discharging. This may help operators more easily identify problems. The discharge thermal energy image can be classified into normal and abnormal cases by the abnormal thermal energy classification model. This facilitates automated anomaly detection and early detection of problems during battery discharge. Combining the charge normal thermal energy image and the discharge normal thermal energy image sequence facilitates creating a sequence of operational normal thermal energy images for overall performance analysis of the battery. Also, combining the charging and discharging abnormal thermal energy images with time sequence is helpful for tracking the abnormal condition of the battery and further diagnosing faults.

Optionally, step S142 specifically includes:

performing Fourier transform on the charging heat energy image set so as to obtain a charging spectrogram;

calculating the frequency spectrum offset of the charging spectrogram, thereby obtaining the charging frequency spectrum offset;

acquiring power supply electrical data, and performing fluctuation calculation on the power supply electrical data so as to acquire the power supply fluctuation;

detecting the abnormal offset of the charged frequency spectrum offset by using the fluctuation quantity of the power supply, so as to obtain the abnormal frequency spectrum offset and the normal frequency spectrum offset;

extracting an abnormal spectrum section of the charging spectrogram according to the abnormal spectrum offset, so as to obtain the charging abnormal spectrogram;

extracting a normal spectrum section of the charging spectrogram according to the normal spectrum offset, so as to obtain the charging normal spectrogram;

and performing inverse Fourier transform on the abnormal charging spectrogram and the normal charging spectrogram, so as to obtain a normal charging thermal energy image and an abnormal charging thermal energy image.

The Fourier transform in the invention converts the time-domain charging thermal energy image into a frequency-domain charging spectrogram. This helps to analyze the frequency distribution of the battery as it operates, possibly revealing specific thermal energy variations in the frequency domain. Calculating the spectral offset may identify the offset of a particular frequency component in the spectrogram. This helps detect changes in the frequency content, which reflects problems or changes in the interior of the battery. The power supply electrical data may provide information on the current and voltage during battery charging. Calculating the amount of fluctuation helps to understand the power stability and whether there is an impact of power fluctuation on the charging process. By correlating with the amount of power supply fluctuation, it is possible to detect whether or not there is an abnormal spectral shift amount. This may help identify anomalies in the battery charging process such as power instability or other disturbances. Extracting the abnormal spectrum segment may help separate out the portion of the spectrum associated with the abnormal frequency offset. This helps to more clearly visualize anomalies in the battery charging process. Extracting the normal spectrum segment may separate the portion associated with the normal frequency offset from the spectrum graph. This helps identify the frequency components in a normal charge situation. The inverse fourier transform restores the spectrogram of the frequency domain to a thermal image of the time domain. This helps to convert the spectral analysis results into a visual image for further analysis of anomalies during charging.

Optionally, step S2 specifically includes:

step S21: performing three-dimensional spatial interpolation according to the classified thermal energy images, so as to obtain a three-dimensional temperature distribution map;

step S22: extracting geometric structure data from the three-dimensional temperature distribution map to obtain geometric structure data;

step S23: constructing a lithium battery geometric model according to the geometric structure data;

step S24: extracting a lithium ion concentration map and a current distribution map from a lithium battery multi-physical field image set, thereby obtaining a lithium ion concentration map and a current distribution map;

step S25: and carrying out multi-physical field coupling simulation on the three-dimensional temperature distribution diagram, the lithium ion concentration diagram and the current distribution diagram according to the lithium battery geometric model, thereby obtaining three-dimensional temperature field simulation data.

The invention can generate a continuous three-dimensional temperature distribution map from discrete classified thermal energy images through interpolation. This helps to achieve a more accurate representation of the temperature field, providing high resolution data for subsequent analysis. Extracting geometry data may identify the shape, size, and internal structure of the battery. This is critical to establishing an accurate cell geometry model and facilitates subsequent multi-physical field simulation. The accurate geometric model is the basis of the battery simulation. It allows simulation of the distribution of parameters such as internal temperature, current, ion concentration, etc. of the battery and can be used to predict battery performance and safety. Extraction of lithium ion concentration and current profiles provides critical physical information inside the battery. The lithium ion concentration map can be used to analyze the distribution of lithium ions in the battery and the current profile can be used to understand the current density distribution. Both are inputs to the multiple physical field simulation. The multi-physical field simulation couples different physical parameters (temperature, lithium ion concentration, current and the like) together to simulate the complex behavior of the battery. This helps predict the performance of the battery under different operating conditions, identifying potential hot spot areas, safety issues, and battery life impact factors.

Optionally, step S24 specifically includes:

performing temperature numerical simulation according to the three-dimensional temperature distribution map so as to obtain simulated temperature distribution data;

performing lithium ion concentration numerical simulation on the lithium ion concentration graph so as to obtain simulated concentration distribution data;

performing current density numerical simulation based on the current distribution diagram, thereby obtaining simulated current density distribution data;

constructing three-dimensional simulation distribution data according to the simulation temperature distribution data, the simulation concentration distribution data and the simulation current density distribution data;

performing coupling parameter calculation according to the three-dimensional temperature distribution diagram, the lithium ion concentration diagram and the current distribution diagram, so as to obtain multiple physical field coupling parameters;

and carrying out multi-physical field coupling simulation on the three-dimensional temperature distribution map based on the three-dimensional simulation distribution data and the multi-physical field coupling parameters, thereby obtaining three-dimensional temperature field simulation data.

The invention can predict the temperature distribution in the battery according to the actual three-dimensional temperature distribution image through numerical simulation. This helps to understand the temperature change of the battery under different conditions, as well as potential hot spot areas. Numerical modeling of lithium ion concentration profiles can help understand the distribution of lithium ions within a battery. This is important to optimize the charge and discharge performance of the battery and to predict the lithium ion diffusion behavior. The simulated current density distribution helps to understand the current flow inside the battery. This is important for evaluating the performance and safety of the battery and predicting current distribution non-uniformity. Combining the simulated data of different physical parameters together can create comprehensive battery simulation data that is critical to multi-physical field simulation and performance prediction. By calculating the coupling parameters of multiple physical fields, the interaction among various physical processes inside the battery can be known. This helps to more accurately simulate battery behavior and identify possible performance bottlenecks or safety risks. The multi-physical field coupling simulation couples together a plurality of physical parameters such as temperature, lithium ion concentration, current density and the like, so that the behavior of the battery can be more comprehensively understood. This is critical to battery design, optimization, and safety assessment, and can help predict the performance and safety of a battery under different operating conditions.

Optionally, step S3 specifically includes:

calculating working condition data of the lithium battery and three-dimensional temperature field simulation data through a temperature controllable coefficient calculation formula, so as to obtain temperature controllable coefficient data;

the temperature controllable coefficient calculation formula specifically comprises:

；

in the method, in the process of the invention,is a temperature controllable coefficient->For working time->For the initial temperature of the lithium battery, +.>For the cooling temperature of lithium batteries, +.>For the temperature change over time, +.>Maximum temperature for the three-dimensional temperature field simulation data, +.>For the lowest temperature of the three-dimensional temperature field simulation data, +.>Is the average operating temperature of the lithium battery.

The invention constructs a temperature controllable coefficient calculation formula for calculating working condition data and three-dimensional temperature field simulation data of the lithium battery. The formula fully considers influencing the temperature controllable coefficientIs>Initial temperature of lithium cell->Cooling temperature of lithium battery->Temperature variation over time->Maximum temperature of three-dimensional temperature field simulation data +.>Minimum temperature of three-dimensional temperature field simulation data +.>Average operating temperature of lithium battery>A functional relationship is formed:

；

wherein,this is in part the derivative of the natural logarithmic rate of change of lithium battery temperature with time. It measures the rate of change of the temperature of the lithium battery with time and considers the initial temperature +. >And cooling temperature->Is a function of (a) and (b). />This is the lithium battery temperature and the average operating temperature +.>And minimum temperature->Normalized difference between them. It measures the relative position between the current temperature and the average operating temperature. />This is the derivative of the normalized difference between the average operating temperature and the lowest temperature, representing the rate of change of the average operating temperature and the lowest temperature over time. />For normalizing the rate of change between the average operating temperature and the lowest temperature to ensure that in the calculation +.>Is not affected by the time scale. The core of the whole formula is to integrate the product of the two parts from 0 to +.>Wherein->Representative ofThe three-dimensional temperature field simulates the highest temperature of the data. The purpose of this integration is to take into account the change in temperature of the lithium battery over time and correlate with the change between the average operating temperature and the minimum temperature. The result of this equation, TCR, represents the combined effect of various factors on the lithium battery temperature, including initial temperature, cooling temperature, average operating temperature, and time variation, to evaluate the controllability of the lithium battery under different temperature conditions. A high TCR value indicates that the lithium battery is more easily controlled under different temperature conditions, while a low TCR value may mean that the lithium battery is more easily out of control under temperature fluctuations. This helps to evaluate the stability and temperature management performance of the lithium battery system. In the art, actual temperature data and related mathematical methods (e.g., numerical modeling, statistical analysis, etc.) may be used to evaluate temperature control performance. The temperature controllable coefficient can be calculated more accurately by using the temperature controllable coefficient calculation formula provided by the invention.

And dividing the range according to the temperature controllable coefficient data, thereby obtaining the temperature controllable range of the lithium battery.

According to the invention, the temperature sensitivity of the lithium battery can be estimated by calculating the temperature controllable coefficient. This is very important because the performance and safety of lithium batteries are closely related to temperature. After the temperature controllable coefficient data are obtained, the behavior of the battery at different temperatures can be better known, so that the performance and the safety of the battery are better managed. Analyzing and dividing the temperature controllable coefficient data into different ranges may help determine the operating temperature range of the battery. This is critical because the battery may be damaged at too high or too low a temperature, affecting its performance and life. After the temperature controllable range is obtained, the operating conditions of the battery can be better specified to ensure that it operates within a safe and efficient temperature range.

Alternatively, the thermal runaway minimization calculation in step S4 is specifically:

performing thermal runaway probability calculation on the working temperature prediction data through a thermal runaway probability calculation formula, so as to obtain thermal runaway probability;

the thermal runaway probability calculation formula specifically comprises:

；

in the method, in the process of the invention,for the probability of thermal runaway >Is the base of natural logarithm, +.>For the instantaneous operating temperature of a lithium battery, +.>For lithium battery thermal runaway threshold temperature, +.>For the instantaneous current of a lithium battery, +.>For the instantaneous voltage of a lithium battery, +.>For the resistance of lithium batteries, ">To simulate working time, < >>Is the temperature weight;

the invention constructs a thermal runaway probability calculation formula for calculating the thermal runaway probability of the working temperature prediction data. The formula fully considers influencing the thermal runaway probabilityBase of natural logarithm of>Instantaneous operating temperature of lithium batteryLithium battery thermal runaway threshold temperature +.>Instantaneous current of lithium cell->Instantaneous voltage of lithium cell->Resistance of lithium cell->Simulation of the working time->Temperature weight->A functional relationship is formed:

；

wherein,this section represents the instantaneous operating temperature of the lithium battery +.>And thermal runaway threshold temperature->Difference between them. If the current temperature is above the thermal runaway threshold temperature, the difference is positive, otherwise negative. />This parameter is used to adjust the influence of temperature on the probability of thermal runaway for temperature weighting. Greater->Value tableThe temperature is shown to have a greater impact on thermal runaway. />This part comprises the instantaneous current of the lithium battery +.>And transient voltage- >Taking the natural logarithm of the ratio. This section represents the effect of current and voltage on the probability of thermal runaway, where the greater the current, the smaller the voltage, and the probability of thermal runaway may increase. />This part comprises the resistance of the lithium battery +.>And the rate of change of the voltage over time +.>Square root and fourth root. This section represents the effect of the internal resistance of the battery and the rate of change of voltage on the probability of thermal runaway. Larger resistances and voltage rates of change may increase the risk of thermal runaway. The whole formula combines these factors together and calculates the thermal runaway probability by a series of mathematical operations>. This probability value is used to evaluate the likelihood of thermal runaway of the lithium battery under given conditions. A high probability of thermal runaway indicates that the lithium battery is more susceptible to thermal runaway under the current operating conditions, while a low probability indicates a lower risk. This formula can be used to help evaluate and manage the safety of lithium batteries. In the art, calculating the probability of thermal runaway is an important safety assessment step to determine the probability that a lithium battery may experience dangerous situations such as overheating, explosion or fire under certain conditions. Calculating the thermal runaway probability generally involves numerical modeling, establishing mathematical models, and the like. Thermal runaway probability calculation by using the present invention The formula can calculate the thermal runaway probability more accurately.

Classifying and calculating the working temperature prediction data according to the thermal runaway probability so as to obtain thermal runaway temperature prediction data and normal temperature prediction data;

and carrying out probability minimization adjustment on the thermal runaway temperature prediction data and the normal temperature prediction data according to the temperature controllable range of the lithium battery, thereby obtaining thermal runaway adjustment data.

According to the invention, through thermal runaway probability calculation, the possibility of thermal runaway of the lithium battery at a given working temperature can be quantified. This is critical because thermal runaway can cause the battery to fire or explode, posing a threat to personal safety and property. Accurate thermal runaway probability calculations can help you identify potential risks and take appropriate measures to prevent accidents. The working temperature prediction data is divided into thermal runaway temperature prediction data and normal temperature prediction data according to the thermal runaway probability, so that it is clear which conditions may lead to thermal runaway under given conditions. This classification helps focus on the high risk temperature range in order to take precautions early to ensure safe operation of the battery system. And carrying out probability minimization adjustment on the thermal runaway temperature prediction data and the normal temperature prediction data according to the controllable range of the lithium battery temperature, so that the thermal runaway probability can be reduced to the minimum. This adjustment ensures that the lithium battery operates within its safe operating temperature range, minimizing the probability of thermal runaway. By this adjustment, the lithium battery system can be used more safely and more reliably in practical applications.

Optionally, the present disclosure further provides a lithium ion battery temperature control system, configured to execute the above lithium ion battery temperature control method, where the lithium ion battery temperature control system includes:

the abnormal heat energy detection module is used for acquiring a lithium battery multi-physical-field image set and lithium battery working condition data through a preset sensor, and detecting abnormal heat energy of the lithium battery multi-physical-field image set so as to obtain a classified heat energy image set;

the physical field simulation module is used for constructing a three-dimensional temperature distribution map according to the classified heat energy image set, and carrying out multi-physical field coupling simulation on the three-dimensional temperature distribution map so as to obtain three-dimensional temperature field simulation data;

the temperature controllable range calculation module is used for calculating the temperature controllable range of the working condition data and the three-dimensional temperature field simulation data of the lithium battery so as to obtain the temperature controllable range of the lithium battery;

the prediction model construction module is used for constructing a lithium battery temperature prediction model according to the three-dimensional temperature field simulation data;

the battery working temperature prediction module is used for acquiring a real-time lithium battery thermal energy image, and predicting the battery working temperature by utilizing the lithium battery temperature prediction model to the real-time lithium battery thermal energy image and the lithium battery working condition data so as to acquire working temperature prediction data;

And the temperature adjusting module is used for carrying out thermal runaway minimization calculation on the working temperature prediction data according to the lithium battery temperature controllable range so as to obtain thermal runaway adjusting data, and carrying out adaptive correction on the lithium battery working condition data according to the thermal runaway adjusting data so as to obtain the lithium battery temperature adjusting data.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic flow chart of the temperature control method of the lithium ion battery;

FIG. 2 is a detailed step flow chart of step S1 of the present invention;

FIG. 3 is a detailed flowchart illustrating the step S13 of the present invention;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, referring to fig. 1 to 3, the present invention provides a temperature control method for a lithium ion battery, the method comprising the following steps:

in this embodiment, a preset sensor (such as a temperature sensor, a current sensor, etc.) is used to obtain a multi-physical-field image set and working condition data of the lithium battery. Such data may include temperature, current, voltage, resistance, etc. of the battery. Abnormal thermal energy detection is then performed on the multi-physical field image set to identify any abnormal thermal energy distribution. This may be achieved by image processing techniques, machine learning algorithms or deep learning models. For example, a neural network is trained to detect abnormal thermal energy distribution, which may be caused by internal faults or abnormal operating conditions of the battery.

In this embodiment, a three-dimensional temperature profile is constructed using the classified thermal energy image set. This can be achieved by superimposing multiple thermal images together and taking into account their position in three-dimensional space. And then, performing multi-physical field coupling simulation, and coupling the three-dimensional temperature distribution map with multi-physical field data of the lithium battery. This means that interactions between parameters such as temperature, current, voltage etc. will be taken into account to more accurately simulate the behaviour of a lithium battery.

in the embodiment, the temperature controllable range of the lithium battery is calculated by using the working condition data of the lithium battery and the three-dimensional temperature field simulation data. This includes determining a safe temperature range within which the lithium battery can operate normally without thermal runaway. This range is generally bounded by upper and lower temperatures beyond which thermal runaway may result.

in the embodiment, a temperature prediction model of the lithium battery is constructed based on three-dimensional temperature field simulation data. This model may be a mathematical model, such as a thermal conduction equation or a battery thermal model, or a predictive model may be trained using machine learning techniques to predict temperature changes based on battery operating condition data.

in this embodiment, a lithium battery temperature prediction model is used to obtain working temperature prediction data of a real-time lithium battery thermal energy image. This requires real-time monitoring of the operating mode data of the lithium battery and inputting it into the predictive model to obtain a temperature prediction under the current operating conditions.

In this embodiment, the working temperature prediction data is analyzed according to the controllable range of the lithium battery temperature, so as to calculate the probability of thermal runaway. If the probability is high, measures may be taken to reduce the risk of thermal runaway, such as reducing the current or stopping charging. And then, carrying out adaptive correction on the working condition data of the lithium battery according to the result of the thermal runaway minimization calculation so as to ensure that the lithium battery works in a controllable temperature range. This may include adjusting charge and discharge rates, changing settings of the cooling system, etc.

Optionally, step S1 specifically includes:

various sensors, such as temperature, current, voltage, pressure and humidity sensors, are used in this embodiment to monitor a plurality of physical parameters of the lithium battery in real time. These sensors constantly collect data including temperature distribution, current charge and discharge conditions, voltage changes, internal pressure and humidity, etc. These data provide comprehensive status information of the battery for subsequent analysis.

the sensor data obtained from the sensor is converted into a thermal image in this embodiment. For example, by means of data from a temperature sensor, a temperature distribution image can be created showing the temperature conditions of different parts of the battery. These images reflect the thermal distribution inside the cell and provide the basis for subsequent analysis.

The present embodiment distinguishes between an idle state and an operating state of the battery. By analyzing the thermal energy images, it is possible to identify which images represent that the battery is in an idle state and which represent that the battery is in operation. This facilitates the application of subsequent anomaly detection and analysis in the correct context.

the present embodiment focuses on the battery thermal image in the idle state. By using an anomaly detection algorithm, abnormal idle thermal images can be identified that may indicate battery problems such as overheating or abnormal temperature distribution. At the same time, it is also possible to determine which images are normal for comparison and reference.

in this embodiment, the battery thermal energy image in the operating state is used. Anomaly detection techniques are used to detect abnormal thermal energy images in an operating state, which anomalies may relate to battery performance problems or potential failures. At the same time, it is also possible to determine which images represent normal operating conditions.

In this embodiment, the idle abnormal thermal energy image, the idle normal thermal energy image, the abnormal thermal energy image and the normal thermal energy image are combined according to the time sequence, so that a classification set including the thermal energy images of the lithium battery in different states can be obtained. This classification set can be used for further analysis and decision making to identify abnormal conditions of the battery and take appropriate measures to ensure the safety and reliability of the battery.

Optionally, step S13 specifically includes:

in this embodiment, time sequence information is extracted from the idle thermal energy image set, that is, thermal energy changes of the battery at different time points are observed. By comparing and analyzing the time series data, the idle state of long time and short time can be identified. For example, if the battery remains at a relatively constant temperature for a longer period of time, it is identified as being idle for a long period of time; and if there is a short time temperature fluctuation, it is identified as short idle.

in this embodiment, the time proportion is determined according to the data acquisition frequency of the sensor, then the idle thermal energy image set is segmented according to the time proportion, and the local temperature extremum is extracted in each segment. These extreme values represent the highest and lowest points of temperature, which help determine the temperature fluctuation range of the battery. These local temperature extremum data are important in subsequent analysis.

in this embodiment, the temperature gradient, that is, the rate of change of the temperature is calculated based on the local temperature extremum and the idle time series data. These local temperature gradient data reflect the trend of temperature change inside the battery. For example, if the temperature gradient is large, it may indicate that there is an abnormal condition inside the battery, and further inspection is required.

in this embodiment, the local temperature gradient data is statistically analyzed to identify high-frequency and low-frequency temperature gradients. A high frequency temperature gradient may indicate rapid fluctuations in the internal temperature of the battery, while a low frequency temperature gradient may indicate a slower temperature change. These data help us to get a finer understanding of the battery status.

in this embodiment, short idle time series data and high frequency temperature gradient data are used to screen the idle thermal image set. If the battery remains at a steady temperature for a short period of time and there is no abnormal high frequency temperature gradient, then these images are identified as idle normal thermal images.

In this embodiment, the abnormal image in the idle thermal image set is screened out according to the long idle time sequence data and the low-frequency temperature gradient data. If the battery is maintained at a relatively constant temperature for a longer period of time, but there is a low frequency temperature gradient change, this may indicate a potential problem with the battery, identifying it as an idle abnormal thermal energy image. These images may serve as a basis for further fault diagnosis and maintenance.

Optionally, step S14 specifically includes:

parameters such as battery voltage, current, etc. are used in this embodiment to distinguish between charge and discharge states. In the charged state, the voltage and current are usually positive values, while in the discharged state the current is negative. By monitoring these parameters, the working thermal energy image set can be divided into two subsets.

in this embodiment, signal processing techniques, such as fourier transforms, are used to convert the thermal energy image into the spectral domain. Then, an abnormal pattern in the spectrum, such as an abnormal frequency component or an amplitude variation in the spectrum, may be detected. If an anomaly is detected, the image is marked as a charging anomaly thermal energy image, otherwise, the image is marked as a charging normal thermal energy image.

in this embodiment, an abnormal thermal energy classification model is constructed using the charged normal thermal energy image and the charged abnormal thermal energy image. The classification model is trained using the charged normal thermal energy image and the charged abnormal thermal energy image. This model may be a machine learning algorithm, such as a support vector machine or deep neural network, for automatically classifying thermal images of the battery in charge.

in this embodiment, the discharge thermal energy image is converted into a spectrum domain, and high frequency components in the spectrum are calculated. These high frequency component components may be related to battery performance, such as heat distribution inside the battery. By counting the high frequency components, the thermal characteristics of each component during discharge can be understood.

in this embodiment, the temperature change of each component in the discharge thermal energy image is analyzed, and the temperature peak value of each component is found. These peaks may have important implications for battery performance and safety.

the temperature peak threshold is set empirically or under expert direction in this embodiment and is marked as abnormal if the temperature peak of the component exceeds the threshold. These abnormal temperature peaks may indicate problems inside the battery, such as overheating or temperature non-uniformity.

in this embodiment, an abnormal component temperature peak is combined with a high-frequency thermal energy component to generate an abnormal discharge thermal energy image. These images may show which components are affected under abnormal conditions and changes in their thermal characteristics.

in this embodiment, the abnormal discharge thermal energy image is input into the abnormal thermal energy classification model constructed previously, and the model automatically identifies which images belong to the abnormal discharge condition. This helps to detect battery problems early and take action.

In this embodiment, the charging normal thermal energy image and the discharging normal thermal energy image are time-sequentially combined to obtain the working normal thermal energy image. And the charging abnormal thermal energy image and the discharging abnormal thermal energy image are combined in time sequence to obtain a working abnormal thermal energy image.

Optionally, step S142 specifically includes:

in this embodiment, the charged thermal image set is converted into a gray image, so as to ensure consistency of data formats. A fourier transform is performed on each thermal image, converting the time domain signal into a frequency domain signal. And acquiring a spectrogram from the Fourier transform result, wherein the spectrogram represents amplitude information of different frequency components.

in this embodiment, a spectrum in a normal state is selected from the generated spectrum patterns as a reference. The amplitude of each frequency component is compared and the offset thereof relative to the reference spectrum is calculated.

in this embodiment, the power supply electrical data is used to calculate the fluctuation amount of the voltage and the current, such as standard deviation, for evaluating the stability of the power supply.

in this embodiment, a threshold is set or a statistical method is used to correlate the spectrum offset with the amount of power supply fluctuation, and detect whether an abnormal spectrum offset exists.

in this embodiment, according to the detection result of the abnormal spectrum offset, the corresponding abnormal spectrum segment in the spectrogram is located. An abnormal spectrum segment is extracted from the spectrogram, and the segment may contain problems or abnormalities in the charging process.

in this embodiment, according to the normal spectrum offset, a corresponding normal spectrum segment in the spectrogram is located. Normal spectrum segments are extracted from the spectrogram, which segments represent normal states of charge.

In this embodiment, inverse fourier transform is performed on the abnormal spectrogram and the normal spectrogram, and the frequency domain signal is restored to the time domain signal. And obtaining an abnormal charging thermal energy image and a normal charging thermal energy image, wherein the images show thermal energy distribution in abnormal and normal charging states.

Optionally, step S2 specifically includes:

interpolation techniques are used in this embodiment to convert the classified thermal image data into a continuous three-dimensional temperature profile. One common method of interpolation is to use three-dimensional spline interpolation. Firstly, gridding the classified thermal energy image data points in a three-dimensional space, and then filling blank areas in the grids by using a spline interpolation method to generate continuous three-dimensional temperature distribution. For example, the classified thermal energy image data points are placed in a three-dimensional grid of coordinates, where each grid cell represents a discrete temperature value. The data points are interpolated using a three-dimensional spline interpolation algorithm, such as B-spline or cubic Hermite interpolation, to generate a continuous temperature distribution. And according to the data characteristics and the required precision, adjusting parameters of an interpolation algorithm to ensure the fitting degree of the generated three-dimensional temperature distribution map and the original data. Optionally, the generated three-dimensional temperature field is visualized in order to better understand the spatial characteristics of the temperature distribution.

In this embodiment, the battery region is separated from the surrounding environment by a suitable threshold segmentation method according to the numerical characteristics of the temperature distribution map. The contour and geometric features of the cell, such as length, width, height, volume and shape, are extracted by image processing techniques, such as connected component analysis or edge detection. The exact location of the battery in three dimensions is determined, typically by calibration or relative coordinate calculations. The extracted geometry data is saved as a three-dimensional model of the battery, which may be a three-dimensional point cloud, CAD model, or other data format suitable for the application.

in this embodiment, CAD software or a three-dimensional modeling tool is used to create a three-dimensional model of the battery from the extracted geometry data. Ensuring that the geometric model conforms to the shape and size of the actual cell, key components such as electrodes, separator, electrolyte, etc. may be included in the model. An appropriate file format is selected to hold the geometric model, such as STL, STEP, or other supported formats. And verifying the model according to the modeled geometric data to ensure the accuracy and consistency of the model.

In this embodiment, preprocessing is performed on the multiple physical field images, including denoising, image enhancement, and correction, to ensure image quality. Lithium ion concentration profile information is extracted from the multi-physical field image using image processing techniques such as thresholding or feature extraction. Using the current sensor data or the electric field simulation results, the current distribution information is mapped onto the multi-physical field image to obtain a current profile. The extracted lithium ion concentration map and the current distribution map are saved as corresponding data files or image files for subsequent use.

In this embodiment, appropriate simulation software, such as COMSOL Multiphysics, ANSYS, etc., is selected to support multiple physical field coupling simulation. And a geometric model of the lithium battery is imported, so that the model can accurately reflect the structure of the battery. Initial and boundary conditions of the temperature field are defined. This may include heat dissipation conditions of the battery surface, current input conditions, and the like. Physical parameters of lithium ion diffusion and migration, such as diffusion coefficient, mobility, etc., are set. The input conditions of the current field are set, and factors such as the conductivity of the electrode and the electrolyte are considered. The temperature field, the lithium ion concentration field and the current field are coupled. This can be achieved by setting the coupling equation and formulating the corresponding physical law. Consider the thermoelectric effect, i.e. the thermal effect that occurs when a current passes through the inside of the battery, which will affect the evolution of the temperature field. And solving the established multi-physical field coupling model by using a numerical solver. This will generate a time-evolving three-dimensional temperature field, lithium ion concentration profile and current profile throughout the battery interior. And extracting simulation results, including a three-dimensional temperature field, a lithium ion concentration map and a current distribution map. Post-processing of the results is performed using data analysis tools (e.g., MATLAB, python, etc.) to obtain key performance metrics and trends. The simulation results are visualized, such as generating a timing diagram, a three-dimensional surface diagram, etc., to better understand the coupled evolution of the physical field inside the battery.

Optionally, step S24 specifically includes:

in this embodiment, in simulation software, a three-dimensional geometric model of a lithium battery is imported. Initial temperature distribution and boundary conditions, such as ambient temperature and heat dissipation conditions, are set. The time evolution of temperature was simulated using a thermal conduction equation. And selecting a proper numerical method (a finite element method or a finite difference method) and a time step to carry out numerical solution so as to obtain the simulated temperature distribution data.

initial conditions and boundary conditions for lithium ion diffusion and migration are set in this example, including initial concentration in the battery and lithium ion implantation conditions of the electrode. The evolution of lithium ion concentration was simulated using a diffusion-migration equation. And selecting a proper numerical method and grid density, and carrying out numerical solution to obtain simulated concentration distribution data.

in this embodiment, a current input condition, that is, a current density distribution of the battery is set. This may be determined according to the operating state and use of the battery. The distribution of current density was simulated using a current transfer equation. And selecting a proper numerical method and grid density, and carrying out numerical solution to obtain analog current density distribution data.

in this embodiment, the simulated temperature distribution data, the simulated concentration distribution data, and the simulated current density distribution data are integrated into a common data structure, and three-dimensional simulated distribution data can be constructed. Each data point includes values for temperature, lithium ion concentration, and current density. These data will be used for subsequent multi-physical field coupling simulations.

in this embodiment, a coupling parameter model is established according to the three-dimensional temperature distribution diagram, the lithium ion concentration diagram and the current distribution diagram, and the mutual influence among the temperature, the lithium ion concentration and the current density is considered. These parameters are calculated by numerical methods, such as the finite element method. These parameters will be used to couple the different physical fields together.

The temperature, lithium ion concentration and current density distribution data are coupled together using the established multi-physical field coupling model in this embodiment. And solving the multi-physical-field model through a numerical solver to obtain simulation data of the three-dimensional temperature field. This simulation data will reflect the evolution of the internal temperature of the lithium battery under different operating conditions.

Optionally, step S3 specifically includes:

in the embodiment, temperature controllable coefficient calculation is performed on working condition data of the lithium battery and three-dimensional temperature field simulation data through a temperature controllable coefficient calculation formula.

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The invention constructs a temperature controllable coefficient calculation formula for calculating working condition data and three-dimensional temperature field simulation data of the lithium battery. The formula fully considers influencing the temperature controllable coefficientIs>Initial temperature of lithium cell->Of lithium batteriesCooling temperature->Temperature variation over time->Maximum temperature of three-dimensional temperature field simulation data +.>Minimum temperature of three-dimensional temperature field simulation data +. >Average operating temperature of lithium battery>A functional relationship is formed:

；

wherein,this is in part the derivative of the natural logarithmic rate of change of lithium battery temperature with time. It measures the rate of change of the temperature of the lithium battery with time and considers the initial temperature +.>And cooling temperature->Is a function of (a) and (b). />This is the lithium battery temperature and the average operating temperature +.>And minimum temperature->Normalized difference between them. It measures the relative position between the current temperature and the average operating temperature. />This is the derivative of the normalized difference between the average operating temperature and the lowest temperature, representing the rate of change of the average operating temperature and the lowest temperature over time. />For normalizing the rate of change between the average operating temperature and the lowest temperature to ensure that in the calculation +.>Is not affected by the time scale. The core of the whole formula is to integrate the product of the two parts from 0 to +.>Wherein->Representing the highest temperature of the three-dimensional temperature field simulation data. The purpose of this integration is to take into account the change in temperature of the lithium battery over time and correlate with the change between the average operating temperature and the minimum temperature. The result of this equation, TCR, represents the combined effect of various factors on the lithium battery temperature, including initial temperature, cooling temperature, average operating temperature, and time variation, to evaluate the controllability of the lithium battery under different temperature conditions. A high TCR value indicates that the lithium battery is more easily controlled under different temperature conditions, while a low TCR value may mean that the lithium battery is more easily out of control under temperature fluctuations. This helps to evaluate the stability and temperature management performance of the lithium battery system. In the art, actual temperature data and related mathematical methods (e.g., numerical modeling, statistical analysis, etc.) may be used to evaluate temperature control performance. The temperature controllable coefficient can be calculated more accurately by using the temperature controllable coefficient calculation formula provided by the invention.

The temperature range of the lithium battery is defined by the minimum allowable temperature and the maximum allowable temperature of the battery in this embodiment. The temperature range is divided into a plurality of temperature segments, for example one segment every 5 degrees celsius. And calculating statistical information such as the mean value, standard deviation and the like of the temperature controllable coefficients for each temperature segment. Based on the statistical information, upper and lower limit values of the controllable coefficient are determined, and these values are compared with the temperature range of the lithium battery. This allows to determine which temperature ranges the controllable coefficients are within acceptable ranges and which are not.

in this embodiment, the operation temperature prediction data is calculated by a thermal runaway probability calculation formula.

The thermal runaway probability calculation formula specifically comprises:

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in the method, in the process of the invention,for the probability of thermal runaway>Bottom of natural logarithmCount (n)/(l)>For the instantaneous operating temperature of a lithium battery, +.>For lithium battery thermal runaway threshold temperature, +.>For the instantaneous current of a lithium battery, +.>For the instantaneous voltage of a lithium battery, +.>For the resistance of lithium batteries, ">To simulate working time, < >>Is the temperature weight;

；

wherein,this section represents the instantaneous operating temperature of the lithium battery +. >And thermal runaway threshold temperature->Difference between them. If the current temperature is above the thermal runaway threshold temperature, the difference is positive, otherwise negative. />This parameter is used to adjust the influence of temperature on the probability of thermal runaway for temperature weighting. Greater->The value indicates that the temperature has a greater influence on thermal runaway. />This part comprises the instantaneous current of the lithium battery +.>And transient voltage->Is taken from the ratio ofBut log. This section represents the effect of current and voltage on the probability of thermal runaway, where the greater the current, the smaller the voltage, and the probability of thermal runaway may increase. />This part comprises the resistance of the lithium battery +.>And the rate of change of the voltage over time +.>Square root and fourth root. This section represents the effect of the internal resistance of the battery and the rate of change of voltage on the probability of thermal runaway. Larger resistances and voltage rates of change may increase the risk of thermal runaway. The whole formula combines these factors together and calculates the thermal runaway probability by a series of mathematical operations>. This probability value is used to evaluate the likelihood of thermal runaway of the lithium battery under given conditions. A high probability of thermal runaway indicates that the lithium battery is more susceptible to thermal runaway under the current operating conditions, while a low probability indicates a lower risk. This formula can be used to help evaluate and manage the safety of lithium batteries. In the art, calculating the probability of thermal runaway is an important safety assessment step to determine the probability that a lithium battery may experience dangerous situations such as overheating, explosion or fire under certain conditions. Calculating the thermal runaway probability generally involves numerical modeling, establishing mathematical models, and the like. By using the thermal runaway probability calculation formula provided by the invention, the thermal runaway probability can be calculated more accurately.

the operating temperature prediction data is correlated with the corresponding thermal runaway probability in this embodiment. A classification model is trained using machine learning methods, such as random forest or support vector machines, to classify temperature data into two classes: thermal runaway and normal. This model can predict from the input operating temperature data that it is likely to be in a thermal runaway or normal state.

In this embodiment, to ensure that the thermal runaway temperature prediction data and the normal temperature prediction data are within the controllable range of the lithium battery temperature, an optimization algorithm, such as bayesian optimization, may be used to adjust these temperature values to minimize the probability of out-of-range.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The lithium ion battery temperature control method is characterized by comprising the following steps:

wherein, step S3 includes:

；

in the method, in the process of the invention,is a temperature controllable coefficient->For working time->For the initial temperature of the lithium battery, +.>For the cooling temperature of lithium batteries, +.>For the temperature change over time, +.>Maximum temperature for the three-dimensional temperature field simulation data, +. >For the lowest temperature of the three-dimensional temperature field simulation data, +.>The average working temperature of the lithium battery;

dividing the range according to the temperature controllable coefficient data, thereby obtaining a temperature controllable range of the lithium battery;

step S6: performing thermal runaway minimization calculation on the working temperature prediction data according to the lithium battery temperature controllable range so as to obtain thermal runaway adjustment data, and performing adaptive correction on the lithium battery working condition data according to the thermal runaway adjustment data so as to obtain lithium battery temperature adjustment data;

wherein, step S6 includes:

the thermal runaway probability calculation formula specifically comprises:

；

in the method, in the process of the invention,for the probability of thermal runaway>Is the base of natural logarithm, +.>For the instantaneous operating temperature of a lithium battery, +. >For lithium battery thermal runaway threshold temperature, +.>For the instantaneous current of a lithium battery, +.>For the instantaneous voltage of a lithium battery, +.>For the resistance of lithium batteries, ">To simulate working time, < >>Is the temperature weight;

2. The method according to claim 1, wherein step S1 is specifically:

3. The method according to claim 2, wherein step S13 is specifically:

4. The method according to claim 3, wherein step S14 is specifically:

5. The method according to claim 4, wherein step S142 is specifically:

6. The method according to claim 5, wherein step S2 is specifically:

7. The method according to claim 6, wherein step S24 is specifically:

8. A lithium-ion battery temperature control system for performing the lithium-ion battery temperature control method of claim 1, the lithium-ion battery temperature control system comprising: