Method and system for controlling running temperature of power lithium battery

Abstract

The invention provides a power lithium battery operation temperature control method and system. The method comprises the following steps: obtaining a reaction enthalpy change matrix corresponding to charge state data of a power lithium battery and an acoustic signal, combining operation record data to pre-train a lithium battery heat production power prediction optimization model, and inputting an actually measured reaction enthalpy change matrix, actually measured lithium dendrite signal amplitude data and operation real-time record data into the model for processing. The heat production power real-time prediction value of the power lithium battery is obtained, the heat production power real-time prediction value is compared with the heat dissipation capacity data, the heat dissipation shortage rate is obtained, and finally an adaptive temperature control strategy is executed through threshold value comparison; according to the method, accurate prediction of the heat production power space distribution of the power lithium battery is realized through fusion of quantum chemical calculation and non-intrusive sensing acoustic detection, and meanwhile, heat production and heat dissipation are compared to generate an adaptive temperature control strategy, so that intelligent control of the running temperature of the power lithium battery is realized, and the prediction precision and reliability are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power lithium battery management, and in particular to a method and system for controlling the operating temperature of a power lithium battery. Background Art

[0002] As a core energy storage component for new energy vehicles, the safety and reliability of power lithium batteries are of great concern. Lithium batteries generate heat during charging and discharging. Improper heat management can lead to decreased battery performance and even serious safety incidents such as thermal runaway. Therefore, accurately predicting lithium battery heat generation is crucial for optimizing thermal management systems and ensuring safe battery operation. Currently, there are several technical solutions for predicting heat generation in power lithium batteries. For example, CATL predicts heat generation based on equivalent circuit fitting. This method relies on empirical parameters, cannot reveal the nature of heat generation at the microscopic level, and lacks accuracy for heat generation prediction under complex operating conditions. BYD relies on low-frequency EIS scanning to detect battery status. This method is invasive, complex, and lacks specificity in the detection frequency band. Tesla uses a purely data-driven black box model for heat generation prediction. This model lacks physical constraints, resulting in poor interpretability of the prediction results and difficulty in ensuring reliable predictions. Therefore, existing power lithium battery heat generation prediction technologies suffer from low prediction accuracy, complex detection methods, and poor model interpretability. A more efficient, accurate, and physically interpretable heat generation prediction method is urgently needed.

[0003] In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for controlling the operating temperature of a power lithium battery. By integrating quantum chemical calculations with acoustic detection of non-invasive sensing, the spatial distribution of heat generation power of the power lithium battery can be accurately predicted. At the same time, heat generation and heat dissipation are compared to generate an adaptive temperature control strategy, thereby realizing intelligent control of the operating temperature of the power lithium battery and improving the prediction accuracy and reliability.

[0005] The present application also provides a method for controlling the operating temperature of a power lithium battery, comprising the following steps: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data, and process them to obtain the reaction enthalpy change matrix; Acquire the acoustic signal of the power lithium battery and process it to obtain the lithium dendrite signal amplitude data; Training the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model; The measured lithium dendrite signal amplitude data is used to obtain the measured reaction enthalpy change matrix, measured acoustic signal and real-time operation recording data of the power lithium battery, and the data is input into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time prediction value of the heat generation power of the power lithium battery; Obtaining heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain a heat dissipation under-ratio; The heat dissipation under-rate is compared with a preset heat dissipation under-rate warning threshold, and a temperature control strategy is executed according to the threshold comparison result.

[0006] Optionally, in the power lithium battery operating temperature control method described in the present application, the step of obtaining the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data, and processing them to obtain a reaction enthalpy change matrix includes: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data; The reaction enthalpy change evaluation data includes initial state energy, delithiation state energy and lithium ion chemical potential; Processing is performed according to the initial state energy, the delithiation state energy and the lithium ion chemical potential to obtain reaction enthalpy change data corresponding to the state of charge data; The state of charge data and the corresponding reaction enthalpy change data are processed to obtain a reaction enthalpy change matrix.

[0007] Optionally, in the power lithium battery operating temperature control method described in the present application, the step of acquiring the acoustic signal of the power lithium battery and processing it to obtain lithium dendrite signal amplitude data includes: Obtain the position coordinates of the preset piezoelectric ceramic sensor and perform vector representation to obtain the corresponding position coordinate vector; Acquiring acoustic signals inside the lithium battery through a preset piezoelectric ceramic sensor according to a preset sampling frequency; The acoustic signal is subjected to wavelet packet transformation to obtain lithium dendrite signal amplitude data.

[0008] Optionally, in the power lithium battery operating temperature control method described in the present application, the training of the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model includes: Obtain the operating record data of the power lithium battery, including historical measured current and historical measured temperature; Preprocessing and vectorizing the historical measured current, historical measured temperature, reaction enthalpy change matrix, lithium dendrite signal amplitude data, and corresponding state of charge data to obtain a heat generation power prediction feature vector; The heat generation power prediction feature vector and the corresponding preset historical heat generation power value are used to train the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model.

[0009] Optionally, in the power lithium battery operating temperature control method described in the present application, obtaining the measured reaction enthalpy change matrix, measured acoustic signal, and real-time operation recording data of the power lithium battery, and inputting them into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time predicted value of the heat generation power of the power lithium battery includes: Obtain the measured reaction enthalpy change matrix, measured acoustic signals and real-time operation recording data of the power lithium battery; Performing a wavelet packet transform on the measured acoustic signal to obtain measured pulse width, measured energy entropy, and measured lithium dendrite signal amplitude data; The real-time recorded data includes real-time current, real-time temperature and real-time state of charge data; The measured reaction enthalpy change matrix, the measured lithium dendrite signal amplitude data, the real-time current and temperature, and the real-time state of charge data are preprocessed and vectorized to obtain a real-time feature vector for heat generation power prediction; The real-time characteristic vector of heat generation power prediction is input into the lithium battery heat generation power prediction optimization model for processing to obtain a real-time prediction value of heat generation power of the power lithium battery.

[0010] Optionally, in the power lithium battery operating temperature control method described in the present application, obtaining the heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain the heat dissipation under-rate includes: Obtain heat dissipation capacity data of power lithium batteries, including air cooling capacity data, liquid cooling capacity data, and phase change material cooling capacity data; If the real-time predicted value of heat generation power is greater than the air cooling capacity data, obtaining a first heat dissipation under-rate; If the real-time predicted value of the heat generation power is greater than the sum of the air cooling capacity data and the liquid cooling capacity data, obtaining a second heat dissipation under-rate; If the real-time predicted value of the heat generation power is greater than the sum of the air cooling capacity data, the liquid cooling capacity data, and the phase change material cooling capacity data, a third heat dissipation deficit rate is obtained.

[0011] Optionally, in the power lithium battery operating temperature control method described in the present application, performing a threshold comparison between the heat dissipation under-rate and a preset heat dissipation under-rate warning threshold, and executing a temperature control strategy according to the threshold comparison result, includes: comparing the first heat dissipation under-rate, the second heat dissipation under-rate, or the third heat dissipation under-rate with a preset heat dissipation under-rate warning threshold; If the first heat dissipation under-ratio is less than or equal to a preset heat dissipation under-ratio warning threshold, adjusting the air cooling parameters; If the first heat dissipation shortage rate is greater than a preset heat dissipation shortage warning threshold, liquid cooling is started; If the second heat dissipation under-ratio is less than or equal to a preset heat dissipation under-rating warning threshold, adjusting the air cooling parameters and the liquid cooling parameters; If the second heat dissipation deficiency rate is greater than a preset heat dissipation deficiency warning threshold, starting phase change material cooling; If the third heat dissipation under-ratio is less than or equal to a preset heat dissipation under-ratio warning threshold, adjusting air cooling parameters, liquid cooling parameters, and phase change material cooling parameters; If the third heat dissipation deficiency rate is greater than a preset heat dissipation deficiency warning threshold, a warning response is output.

[0012] Optionally, the power lithium battery operating temperature control method described in the present application further includes: Obtaining a measured time difference between adjacent piezoelectric ceramic sensors obtaining measured acoustic signals; If the measured pulse width is greater than a preset pulse width threshold, and the measured energy entropy is greater than a preset energy entropy threshold, processing is performed based on the measured time difference and position coordinate vector in combination with a preset sound speed to obtain measured lithium dendrite characteristic position data; The preset heat dissipation device layout is adjusted according to the measured lithium dendrite characteristic position data.

[0013] In a second aspect, the present application provides a power lithium battery operating temperature control system, the system comprising: a memory and a processor, the memory comprising a program for a power lithium battery operating temperature control method, the program for a power lithium battery operating temperature control method, when executed by the processor, implementing the following steps: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data, and process them to obtain the reaction enthalpy change matrix; Acquire the acoustic signal of the power lithium battery and process it to obtain the lithium dendrite signal amplitude data; Training the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model; The measured lithium dendrite signal amplitude data is used to obtain the measured reaction enthalpy change matrix, measured acoustic signal and real-time operation recording data of the power lithium battery, and the data is input into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time prediction value of the heat generation power of the power lithium battery; Obtaining heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain a heat dissipation under-ratio; The heat dissipation under-rate is compared with a preset heat dissipation under-rate warning threshold, and a temperature control strategy is executed according to the threshold comparison result.

[0014] Optionally, in a power lithium battery operating temperature control system described in the present application, the acquiring of state of charge data of the power lithium battery and corresponding reaction enthalpy change data, and processing thereof to obtain a reaction enthalpy change matrix, includes: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data; The reaction enthalpy change evaluation data includes initial state energy, delithiation state energy and lithium ion chemical potential; Processing is performed according to the initial state energy, the delithiation state energy and the lithium ion chemical potential to obtain reaction enthalpy change data corresponding to the state of charge data; The state of charge data and the corresponding reaction enthalpy change data are processed to obtain a reaction enthalpy change matrix.

[0015] From the above, it can be seen that the present application provides a method and system for controlling the operating temperature of a power lithium battery. By integrating quantum chemical calculations with acoustic detection of non-invasive sensing, accurate prediction of the spatial distribution of heat generation power of the power lithium battery is achieved. At the same time, heat generation and heat dissipation are compared to generate an adaptive temperature control strategy, thereby realizing intelligent control of the operating temperature of the power lithium battery and improving prediction accuracy and reliability.

[0016] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a method for controlling the operating temperature of a power lithium battery provided in an embodiment of the present application; Figure 2 A flow chart of obtaining a reaction enthalpy change matrix for a method for controlling the operating temperature of a power lithium battery provided in an embodiment of the present application; Figure 3 A flow chart of obtaining lithium dendrite signal amplitude data for a method for controlling the operating temperature of a power lithium battery provided in an embodiment of the present application; Figure 41 is a high-level flow chart of the methods of various embodiments of the present application, which can be used for power lithium battery operating temperature control methods. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for controlling the operating temperature of a power lithium battery in some embodiments of the present application. This method is used in terminal devices such as computers and mobile phones. This method includes the following steps: S11. Obtaining state of charge data of the power lithium battery and corresponding reaction enthalpy change evaluation data, and processing them to obtain a reaction enthalpy change matrix; S12, obtaining an acoustic signal of the power lithium battery and processing it to obtain lithium dendrite signal amplitude data; S13, training the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model; S14, obtaining the measured reaction enthalpy change matrix, measured acoustic signal and real-time operation recording data of the power lithium battery by measuring the lithium dendrite signal amplitude data, and inputting them into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time prediction value of the heat generation power of the power lithium battery; S15, obtaining heat dissipation capacity data of the power lithium battery, and comparing it with the real-time predicted value of heat generation power to obtain a heat dissipation under-rate; S16: performing a threshold comparison between the heat dissipation under-rate and a preset heat dissipation under-rate warning threshold, and executing a temperature control strategy according to the threshold comparison result.

[0022] It should be noted that in order to achieve precise control of the temperature of the power lithium battery, this embodiment adopts an organic fusion of quantum chemical calculation and acoustic monitoring. First, the power lithium battery is divided into several 1cmx1cm grids. The reaction enthalpy change corresponding to each grid is analyzed based on different state of charge data to construct a reaction enthalpy change matrix. Based on the acoustic signal obtained by the non-invasive sensor, the signal amplitude data of the lithium dendrite characteristic event in each grid is determined. Combined with the operation record data of the power lithium battery, a physical constraint layer is designed to enforce energy conservation. The model is pre-trained, and then the heat generation is predicted based on the measured data during the operation of the power lithium battery to obtain a real-time predicted value of the heat generation power. Then, the heat dissipation capacity of the temperature control system is analyzed and compared with the real-time predicted value of the heat generation power. If the real-time predicted value of the heat generation power is less than or equal to the heat dissipation capacity, normal operation is sufficient. If it is greater than the heat dissipation capacity, an adaptive temperature control strategy is implemented according to different levels. Finally, the position of the preset heat dissipation device is adjusted according to the generated heat generation spatial distribution map to facilitate precise temperature control and heat dissipation.

[0023] Please refer to Figure 2 , Figure 2 This is a flow chart of obtaining a reaction enthalpy change matrix for a power lithium battery operating temperature control method in some embodiments of the present application. According to an embodiment of the present invention, obtaining state of charge data of the power lithium battery and corresponding reaction enthalpy change evaluation data, and processing them to obtain the reaction enthalpy change matrix includes: S21. Obtaining state of charge data of the power lithium battery and corresponding reaction enthalpy change evaluation data; S22, the reaction enthalpy change evaluation data includes initial state energy, delithiation state energy and lithium ion chemical potential; S23, performing processing according to the initial state energy, the delithiation state energy, and the lithium ion chemical potential to obtain reaction enthalpy change data corresponding to the state of charge data; S24. Process the state of charge data and the corresponding reaction enthalpy change data to obtain a reaction enthalpy change matrix.

[0024] It should be noted that in order to evaluate the heat generation of power lithium batteries under different states of charge, the reaction enthalpy change is calculated in combination with density functional theory to obtain the reaction enthalpy change data, that is, the delithiation state energy minus the initial state energy plus the lithium ion chemical potential. Among them, the delithiation state energy and the initial state energy are obtained by those skilled in the art using WASP software to construct a unit cell model of the lithium battery positive electrode material and calculate it using density functional theory. The lithium ion chemical potential is determined by those skilled in the art based on the standard electrode potential of metallic lithium. The state of charge data and the corresponding reaction enthalpy change data are matched one by one to generate a reaction enthalpy change matrix.

[0025] Please refer to Figure 3 , Figure 3 This is a flow chart of obtaining lithium dendrite signal amplitude data for a power lithium battery operating temperature control method in some embodiments of the present application. According to an embodiment of the present invention, obtaining the acoustic signal of the power lithium battery and processing it to obtain the lithium dendrite signal amplitude data includes: S31, obtaining the position coordinates of a preset piezoelectric ceramic sensor, and performing vector representation to obtain a corresponding position coordinate vector; S32, obtaining an acoustic signal inside the lithium battery using a preset piezoelectric ceramic sensor according to a preset sampling frequency; S33. Perform wavelet packet transform on the acoustic signal to obtain lithium dendrite signal amplitude data.

[0026] It should be noted that several piezoelectric ceramic sensors are preset at different positions of the power lithium battery pack to collect acoustic signals in real time according to the preset frequency. The obtained acoustic signals are then transformed with wavelet packets, and the lithium dendrite signal amplitude data is extracted. The lithium dendrite signal amplitude data refers to the signal in the 150-220kHz frequency band, which can reflect the abnormal lithium dendrite puncture event inside the lithium battery.

[0027] According to an embodiment of the present invention, the training of the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model includes: Obtain the operating record data of the power lithium battery, including historical measured current and historical measured temperature; Preprocessing and vectorizing the historical measured current, historical measured temperature, reaction enthalpy change matrix, lithium dendrite signal amplitude data, and corresponding state of charge data to obtain a heat generation power prediction feature vector; The heat generation power prediction feature vector and the corresponding preset historical heat generation power value are used to train the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model.

[0028] It should be noted that in order to improve the accuracy of the prediction, the operating record data of the power lithium battery, including the historical measured current and the historical measured temperature, are monitored at the same time, and then the obtained reaction enthalpy change matrix, lithium dendrite signal amplitude data and the corresponding state of charge data are preprocessed, such as normalizing the reaction enthalpy change matrix and vectorizing it [reaction enthalpy change matrix, lithium dendrite signal amplitude data, historical measured current, historical measured temperature, state of charge data]. Finally, the initial lithium battery heat generation power prediction model is trained, and a physical constraint layer is designed in the model to force energy conservation to ensure that the heat generation power predicted by the model conforms to the basic laws of thermodynamics.

[0029] According to an embodiment of the present invention, obtaining the measured reaction enthalpy change matrix, measured acoustic signal, and real-time operation recording data of the power lithium battery, and inputting them into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time prediction value of the heat generation power of the power lithium battery includes: Obtain the measured reaction enthalpy change matrix, measured acoustic signals and real-time operation recording data of the power lithium battery; Performing a wavelet packet transform on the measured acoustic signal to obtain measured pulse width, measured energy entropy, and measured lithium dendrite signal amplitude data; The real-time recorded data includes real-time current, real-time temperature and real-time state of charge data; The measured reaction enthalpy change matrix, the measured lithium dendrite signal amplitude data, the real-time current and temperature, and the real-time state of charge data are preprocessed and vectorized to obtain a real-time feature vector for heat generation power prediction; The real-time characteristic vector of heat generation power prediction is input into the lithium battery heat generation power prediction optimization model for processing to obtain a real-time prediction value of heat generation power of the power lithium battery.

[0030] It should be noted that the measured reaction enthalpy change matrix, measured acoustic signal, real-time current, real-time temperature and real-time state of charge data obtained through real-time analysis and processing are pre-processed and vectorized, and then processed in the trained lithium battery heat generation power prediction optimization model to obtain the real-time prediction value of the heat generation power of the power lithium battery. The real-time prediction value of the heat generation power is divided into a 1cmx1cm grid for representation.

[0031] According to an embodiment of the present invention, obtaining the heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain the heat dissipation under-rate includes: Obtain heat dissipation capacity data of power lithium batteries, including air cooling capacity data, liquid cooling capacity data, and phase change material cooling capacity data; If the real-time predicted value of heat generation power is greater than the air cooling capacity data, obtaining a first heat dissipation under-rate; If the real-time predicted value of the heat generation power is greater than the sum of the air cooling capacity data and the liquid cooling capacity data, obtaining a second heat dissipation under-rate; If the real-time predicted value of the heat generation power is greater than the sum of the air cooling capacity data, the liquid cooling capacity data, and the phase change material cooling capacity data, a third heat dissipation deficit rate is obtained.

[0032] It should be noted that the real-time predicted value of heat production power corresponding to each grid is accumulated, and then compared with the air cooling capacity data, liquid cooling capacity data and phase change material cooling capacity data respectively. According to the comparison, the first heat dissipation underrate, the second heat dissipation underrate or the third heat dissipation underrate is obtained, wherein the first heat dissipation underrate refers to the ratio of the difference between the real-time predicted value of heat production power and the air cooling capacity data to the air cooling capacity data. Similarly, the second heat dissipation underrate or the third heat dissipation underrate can be obtained. Air cooling refers to the use of circulating cold air for cooling, liquid cooling refers to the arrangement of cooling plates or cooling pipes to use the circulating flow of coolant for cooling, and phase change material cooling refers to the use of phase change materials to absorb heat for cooling during solid-liquid phase change.

[0033] According to an embodiment of the present invention, comparing the heat dissipation under-rate with a preset heat dissipation under-rate warning threshold, and executing a temperature control strategy according to the threshold comparison result, includes: comparing the first heat dissipation under-rate, the second heat dissipation under-rate, or the third heat dissipation under-rate with a preset heat dissipation under-rate warning threshold; If the first heat dissipation under-ratio is less than or equal to a preset heat dissipation under-ratio warning threshold, adjusting the air cooling parameters; If the first heat dissipation shortage rate is greater than a preset heat dissipation shortage warning threshold, liquid cooling is started; If the second heat dissipation under-ratio is less than or equal to a preset heat dissipation under-rating warning threshold, adjusting the air cooling parameters and the liquid cooling parameters; If the second heat dissipation deficiency rate is greater than a preset heat dissipation deficiency warning threshold, starting phase change material cooling; If the third heat dissipation under-ratio is less than or equal to a preset heat dissipation under-ratio warning threshold, adjusting air cooling parameters, liquid cooling parameters, and phase change material cooling parameters; If the third heat dissipation deficiency rate is greater than a preset heat dissipation deficiency warning threshold, a warning response is output.

[0034] It should be noted that the obtained first heat dissipation under-rate, second heat dissipation under-rate or third heat dissipation under-rate is compared with a preset heat dissipation under-rate warning threshold, and a corresponding temperature control strategy is generated according to the range in which the threshold comparison falls.

[0035] According to an embodiment of the present invention, the further embodiment includes: Obtaining a measured time difference between adjacent piezoelectric ceramic sensors obtaining measured acoustic signals; If the measured pulse width is greater than a preset pulse width threshold, and the measured energy entropy is greater than a preset energy entropy threshold, processing is performed based on the measured time difference and position coordinate vector in combination with a preset sound speed to obtain measured lithium dendrite characteristic position data; The preset heat dissipation device layout is adjusted according to the measured lithium dendrite characteristic position data.

[0036] It should be noted that after a lithium dendrite event occurs at different locations inside the power lithium battery, it is more likely to generate additional heat. In order to reduce the risk of thermal runaway, the acoustic signal is processed by wavelet packet transform, and the measured pulse width obtained is compared with the preset pulse width threshold, and the measured energy entropy is compared with the preset energy entropy threshold. If both are greater than the preset threshold, it is determined that a lithium dendrite event has occurred at this location. The measured time difference of the same acoustic signal propagating to different sensors is counted, and the measured time difference and position coordinate vector are combined with the preset sound speed for processing to obtain the measured lithium dendrite characteristic position data, wherein the preset sound speed is obtained by querying different materials of the power battery shell. Technicians in this field use the arrival time difference (TDOA) algorithm and three sensors to establish a set of equations to solve the event source coordinates and locate the lithium dendrite event source. Afterwards, the preset heat dissipation device layout is adjusted according to the obtained measured lithium dendrite characteristic position data, such as adjusting the density of the heat dissipation device.

[0037] Please refer to Figure 4 , Figure 4 The following is a high-level flow chart of various embodiments of the present application, which can be used for power lithium battery operating temperature control methods. According to an embodiment of the present invention, for example, in step S47, the predicted real-time predicted heat generation power value is compared with the analyzed heat dissipation capacity assessment data, and a corresponding temperature control strategy is generated based on the comparison to achieve intelligent control of the power lithium battery operating temperature.

[0038] The present invention also discloses a power lithium battery operating temperature control system, comprising a memory and a processor. The memory comprises a power lithium battery operating temperature control method program. When the power lithium battery operating temperature control method program is executed by the processor, the following steps are implemented: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data, and process them to obtain the reaction enthalpy change matrix; Acquire the acoustic signal of the power lithium battery and process it to obtain the lithium dendrite signal amplitude data; Training the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model; The measured lithium dendrite signal amplitude data is used to obtain the measured reaction enthalpy change matrix, measured acoustic signal and real-time operation recording data of the power lithium battery, and the data is input into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time prediction value of the heat generation power of the power lithium battery; Obtaining heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain a heat dissipation under-ratio; The heat dissipation under-rate is compared with a preset heat dissipation under-rate warning threshold, and a temperature control strategy is executed according to the threshold comparison result.

[0039] It should be noted that in order to achieve precise control of the temperature of the power lithium battery, this embodiment adopts an organic fusion of quantum chemical calculation and acoustic monitoring. First, the power lithium battery is divided into several 1cmx1cm grids. The reaction enthalpy change corresponding to each grid is analyzed based on different state of charge data to construct a reaction enthalpy change matrix. Based on the acoustic signal obtained by the non-invasive sensor, the signal amplitude data of the lithium dendrite characteristic event in each grid is determined. Combined with the operation record data of the power lithium battery, a physical constraint layer is designed to enforce energy conservation. The model is pre-trained, and then the heat generation is predicted based on the measured data during the operation of the power lithium battery to obtain a real-time predicted value of the heat generation power. Then, the heat dissipation capacity of the temperature control system is analyzed and compared with the real-time predicted value of the heat generation power. If the real-time predicted value of the heat generation power is less than or equal to the heat dissipation capacity, normal operation is sufficient. If it is greater than the heat dissipation capacity, an adaptive temperature control strategy is implemented according to different levels. Finally, the position of the preset heat dissipation device is adjusted according to the generated heat generation spatial distribution map to facilitate precise temperature control and heat dissipation.

[0040] According to an embodiment of the present invention, the step of obtaining the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data, and processing the data to obtain a reaction enthalpy change matrix includes: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data; The reaction enthalpy change evaluation data includes initial state energy, delithiation state energy and lithium ion chemical potential; Processing is performed according to the initial state energy, the delithiation state energy and the lithium ion chemical potential to obtain reaction enthalpy change data corresponding to the state of charge data; The state of charge data and the corresponding reaction enthalpy change data are processed to obtain a reaction enthalpy change matrix.

[0041] It should be noted that in order to evaluate the heat generation of power lithium batteries under different states of charge, the reaction enthalpy change is calculated in combination with density functional theory to obtain the reaction enthalpy change data, that is, the delithiation state energy minus the initial state energy plus the lithium ion chemical potential. Among them, the delithiation state energy and the initial state energy are obtained by those skilled in the art using WASP software to construct a unit cell model of the lithium battery positive electrode material and calculate it using density functional theory. The lithium ion chemical potential is determined by those skilled in the art based on the standard electrode potential of metallic lithium. The state of charge data and the corresponding reaction enthalpy change data are matched one by one to generate a reaction enthalpy change matrix.

[0042] According to an embodiment of the present invention, the step of acquiring the acoustic signal of the power lithium battery and processing it to obtain lithium dendrite signal amplitude data includes: Obtain the position coordinates of the preset piezoelectric ceramic sensor and perform vector representation to obtain the corresponding position coordinate vector; Acquiring acoustic signals inside the lithium battery through a preset piezoelectric ceramic sensor according to a preset sampling frequency; The acoustic signal is subjected to wavelet packet transformation to obtain lithium dendrite signal amplitude data.

[0043] It should be noted that several piezoelectric ceramic sensors are preset at different positions of the power lithium battery pack to collect acoustic signals in real time according to the preset frequency. The obtained acoustic signals are then transformed with wavelet packets, and the lithium dendrite signal amplitude data is extracted. The lithium dendrite signal amplitude data refers to the signal in the 150-220kHz frequency band, which can reflect the abnormal lithium dendrite puncture event inside the lithium battery.

[0044] According to an embodiment of the present invention, the training of the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model includes: Obtain the operating record data of the power lithium battery, including historical measured current and historical measured temperature; Preprocessing and vectorizing the historical measured current, historical measured temperature, reaction enthalpy change matrix, lithium dendrite signal amplitude data, and corresponding state of charge data to obtain a heat generation power prediction feature vector; The heat generation power prediction feature vector and the corresponding preset historical heat generation power value are used to train the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model.

[0045] It should be noted that in order to improve the accuracy of the prediction, the operating record data of the power lithium battery, including the historical measured current and the historical measured temperature, are monitored at the same time, and then the obtained reaction enthalpy change matrix, lithium dendrite signal amplitude data and the corresponding state of charge data are preprocessed, such as normalizing the reaction enthalpy change matrix and vectorizing it [reaction enthalpy change matrix, lithium dendrite signal amplitude data, historical measured current, historical measured temperature, state of charge data]. Finally, the initial lithium battery heat generation power prediction model is trained, and a physical constraint layer is designed in the model to force energy conservation to ensure that the heat generation power predicted by the model conforms to the basic laws of thermodynamics.

[0046] According to an embodiment of the present invention, obtaining the measured reaction enthalpy change matrix, measured acoustic signal, and real-time operation recording data of the power lithium battery, and inputting them into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time prediction value of the heat generation power of the power lithium battery includes: Obtain the measured reaction enthalpy change matrix, measured acoustic signals and real-time operation recording data of the power lithium battery; Performing a wavelet packet transform on the measured acoustic signal to obtain measured pulse width, measured energy entropy, and measured lithium dendrite signal amplitude data; The real-time recorded data includes real-time current, real-time temperature and real-time state of charge data; The measured reaction enthalpy change matrix, the measured lithium dendrite signal amplitude data, the real-time current and temperature, and the real-time state of charge data are preprocessed and vectorized to obtain a real-time feature vector for heat generation power prediction; The real-time characteristic vector of heat generation power prediction is input into the lithium battery heat generation power prediction optimization model for processing to obtain a real-time prediction value of heat generation power of the power lithium battery.

[0047] It should be noted that the measured reaction enthalpy change matrix, measured acoustic signal, real-time current, real-time temperature and real-time state of charge data obtained through real-time analysis and processing are pre-processed and vectorized, and then processed in the trained lithium battery heat generation power prediction optimization model to obtain the real-time prediction value of the heat generation power of the power lithium battery. The real-time prediction value of the heat generation power is divided into a 1cmx1cm grid for representation.

[0048] According to an embodiment of the present invention, obtaining the heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain the heat dissipation under-rate includes: Obtain heat dissipation capacity data of power lithium batteries, including air cooling capacity data, liquid cooling capacity data, and phase change material cooling capacity data; If the real-time predicted value of heat generation power is greater than the air cooling capacity data, obtaining a first heat dissipation under-rate; If the real-time predicted value of the heat generation power is greater than the sum of the air cooling capacity data and the liquid cooling capacity data, obtaining a second heat dissipation under-rate; If the real-time predicted value of the heat generation power is greater than the sum of the air cooling capacity data, the liquid cooling capacity data, and the phase change material cooling capacity data, a third heat dissipation deficit rate is obtained.

[0049] It should be noted that the real-time predicted value of heat production power corresponding to each grid is accumulated, and then compared with the air cooling capacity data, liquid cooling capacity data and phase change material cooling capacity data respectively. According to the comparison, the first heat dissipation underrate, the second heat dissipation underrate or the third heat dissipation underrate is obtained, wherein the first heat dissipation underrate refers to the ratio of the difference between the real-time predicted value of heat production power and the air cooling capacity data to the air cooling capacity data. Similarly, the second heat dissipation underrate or the third heat dissipation underrate can be obtained. Air cooling refers to the use of circulating cold air for cooling, liquid cooling refers to the arrangement of cooling plates or cooling pipes to use the circulating flow of coolant for cooling, and phase change material cooling refers to the use of phase change materials to absorb heat for cooling during solid-liquid phase change.

[0050] According to an embodiment of the present invention, comparing the heat dissipation under-rate with a preset heat dissipation under-rate warning threshold, and executing a temperature control strategy according to the threshold comparison result, includes: comparing the first heat dissipation under-rate, the second heat dissipation under-rate, or the third heat dissipation under-rate with a preset heat dissipation under-rate warning threshold; If the first heat dissipation under-ratio is less than or equal to a preset heat dissipation under-ratio warning threshold, adjusting the air cooling parameters; If the first heat dissipation shortage rate is greater than a preset heat dissipation shortage warning threshold, liquid cooling is started; If the second heat dissipation under-ratio is less than or equal to a preset heat dissipation under-rating warning threshold, adjusting the air cooling parameters and the liquid cooling parameters; If the second heat dissipation deficiency rate is greater than a preset heat dissipation deficiency warning threshold, starting phase change material cooling; If the third heat dissipation under-ratio is less than or equal to a preset heat dissipation under-ratio warning threshold, adjusting air cooling parameters, liquid cooling parameters, and phase change material cooling parameters; If the third heat dissipation deficiency rate is greater than a preset heat dissipation deficiency warning threshold, a warning response is output.

[0051] It should be noted that the obtained first heat dissipation under-rate, second heat dissipation under-rate or third heat dissipation under-rate is compared with a preset heat dissipation under-rate warning threshold, and a corresponding temperature control strategy is generated according to the range in which the threshold comparison falls.

[0052] According to an embodiment of the present invention, the further embodiment includes: Obtaining a measured time difference between adjacent piezoelectric ceramic sensors obtaining measured acoustic signals; If the measured pulse width is greater than a preset pulse width threshold, and the measured energy entropy is greater than a preset energy entropy threshold, processing is performed based on the measured time difference and position coordinate vector in combination with a preset sound speed to obtain measured lithium dendrite characteristic position data; The preset heat dissipation device layout is adjusted according to the measured lithium dendrite characteristic position data.

[0053] It should be noted that after a lithium dendrite event occurs at different locations inside the power lithium battery, it is more likely to generate additional heat. In order to reduce the risk of thermal runaway, the acoustic signal is processed by wavelet packet transform, and the measured pulse width obtained is compared with the preset pulse width threshold, and the measured energy entropy is compared with the preset energy entropy threshold. If both are greater than the preset threshold, it is determined that a lithium dendrite event has occurred at this location. The measured time difference of the same acoustic signal propagating to different sensors is counted, and the measured time difference and position coordinate vector are combined with the preset sound speed for processing to obtain the measured lithium dendrite characteristic position data, wherein the preset sound speed is obtained by querying different materials of the power battery shell. Technicians in this field use the arrival time difference (TDOA) algorithm and three sensors to establish a set of equations to solve the event source coordinates and locate the lithium dendrite event source. Afterwards, the preset heat dissipation device layout is adjusted according to the obtained measured lithium dendrite characteristic position data, such as adjusting the density of the heat dissipation device.

[0054] According to an embodiment of the present invention, for example, in step S47, the predicted real-time predicted value of heat generation power is compared with the heat dissipation capacity evaluation data obtained by analysis, and a corresponding temperature control strategy is generated based on the comparison to achieve intelligent control of the operating temperature of the power lithium battery.

[0055] The present invention discloses a method and system for controlling the operating temperature of a power lithium battery. By integrating quantum chemical calculations with acoustic detection using non-invasive sensing, the system accurately predicts the spatial distribution of heat generation power in the power lithium battery. At the same time, the system compares heat generation with heat dissipation to generate an adaptive temperature control strategy, thereby achieving intelligent control of the operating temperature of the power lithium battery and improving prediction accuracy and reliability.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0057] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0058] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0059] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware related to program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0060] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for controlling the operating temperature of a power lithium battery, characterized in that: The following steps are involved: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data, and process them to obtain the reaction enthalpy change matrix; Acquire the acoustic signal of the power lithium battery and process it to obtain the lithium dendrite signal amplitude data; Training the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model; Obtaining the measured reaction enthalpy change matrix, measured acoustic signals, and real-time operation recording data of the power lithium battery, and inputting them into the lithium battery heat generation power prediction optimization model for processing to obtain a real-time prediction value of the heat generation power of the power lithium battery; Obtaining heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain a heat dissipation under-ratio; The heat dissipation under-rate is compared with a preset heat dissipation under-rate warning threshold, and a temperature control strategy is executed according to the threshold comparison result.

2. The power lithium battery operating temperature control method according to claim 1, characterized in that: The step of obtaining the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data and processing them to obtain a reaction enthalpy change matrix includes: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data; The reaction enthalpy change evaluation data includes initial state energy, delithiation state energy and lithium ion chemical potential; Processing is performed according to the initial state energy, the delithiation state energy and the lithium ion chemical potential to obtain reaction enthalpy change data corresponding to the state of charge data; The state of charge data and the corresponding reaction enthalpy change data are processed to obtain a reaction enthalpy change matrix.

3. The method for controlling the operating temperature of a power lithium battery according to claim 2, wherein: The step of obtaining the acoustic signal of the power lithium battery and processing it to obtain lithium dendrite signal amplitude data includes: Obtain the position coordinates of the preset piezoelectric ceramic sensor and perform vector representation to obtain the corresponding position coordinate vector; Acquiring acoustic signals inside the lithium battery through a preset piezoelectric ceramic sensor according to a preset sampling frequency; The acoustic signal is subjected to wavelet packet transformation to obtain lithium dendrite signal amplitude data.

4. The method for controlling the operating temperature of a power lithium battery according to claim 3, wherein: The initial lithium battery heat generation power prediction model is trained to obtain a trained lithium battery heat generation power prediction optimization model, including: Obtain the operating record data of the power lithium battery, including historical measured current and historical measured temperature; Preprocessing and vectorizing the historical measured current, historical measured temperature, reaction enthalpy change matrix, lithium dendrite signal amplitude data, and corresponding state of charge data to obtain a heat generation power prediction feature vector; The heat generation power prediction feature vector and the corresponding preset historical heat generation power value are used to train the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model.

5. The method for controlling the operating temperature of a power lithium battery according to claim 4, characterized in that: The method of obtaining the measured reaction enthalpy change matrix, measured acoustic signal and real-time operation record data of the power lithium battery and inputting them into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time prediction value of the heat generation power of the power lithium battery includes: Obtain the measured reaction enthalpy change matrix, measured acoustic signals and real-time operation recording data of the power lithium battery; Performing a wavelet packet transform on the measured acoustic signal to obtain measured pulse width, measured energy entropy, and measured lithium dendrite signal amplitude data; The real-time recorded data includes real-time current, real-time temperature and real-time state of charge data; The measured reaction enthalpy change matrix, the measured lithium dendrite signal amplitude data, the real-time current and temperature, and the real-time state of charge data are preprocessed and vectorized to obtain a real-time feature vector for heat generation power prediction; The real-time characteristic vector of heat generation power prediction is input into the lithium battery heat generation power prediction optimization model for processing to obtain a real-time prediction value of heat generation power of the power lithium battery.

6. The method for controlling the operating temperature of a power lithium battery according to claim 5, characterized in that: The obtaining of the heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain the heat dissipation under-ratio includes: Obtain heat dissipation capacity data of power lithium batteries, including air cooling capacity data, liquid cooling capacity data, and phase change material cooling capacity data; If the real-time predicted value of heat generation power is greater than the air cooling capacity data, obtaining a first heat dissipation under-rate; If the real-time predicted value of the heat generation power is greater than the sum of the air cooling capacity data and the liquid cooling capacity data, obtaining a second heat dissipation under-rate; If the real-time predicted value of the heat generation power is greater than the sum of the air cooling capacity data, the liquid cooling capacity data, and the phase change material cooling capacity data, a third heat dissipation deficit rate is obtained.

7. The method for controlling the operating temperature of a power lithium battery according to claim 6, wherein: The step of comparing the heat dissipation under-rate with a preset heat dissipation under-rate warning threshold, and executing a temperature control strategy according to the threshold comparison result, includes: comparing the first heat dissipation under-rate, the second heat dissipation under-rate, or the third heat dissipation under-rate with a preset heat dissipation under-rate warning threshold; If the first heat dissipation under-ratio is less than or equal to a preset heat dissipation under-ratio warning threshold, adjusting the air cooling parameters; If the first heat dissipation shortage rate is greater than a preset heat dissipation shortage warning threshold, liquid cooling is started; If the second heat dissipation under-ratio is less than or equal to a preset heat dissipation under-rating warning threshold, adjusting the air cooling parameters and the liquid cooling parameters; If the second heat dissipation deficiency rate is greater than a preset heat dissipation deficiency warning threshold, starting phase change material cooling; If the third heat dissipation under-ratio is less than or equal to a preset heat dissipation under-ratio warning threshold, adjusting air cooling parameters, liquid cooling parameters, and phase change material cooling parameters; If the third heat dissipation deficiency rate is greater than a preset heat dissipation deficiency warning threshold, a warning response is output.

8. The method for controlling the operating temperature of a power lithium battery according to claim 6, wherein: Also includes: Obtaining a measured time difference between adjacent piezoelectric ceramic sensors obtaining measured acoustic signals; If the measured pulse width is greater than a preset pulse width threshold, and the measured energy entropy is greater than a preset energy entropy threshold, processing is performed based on the measured time difference and position coordinate vector in combination with a preset sound speed to obtain measured lithium dendrite characteristic position data; The preset heat dissipation device layout is adjusted according to the measured lithium dendrite characteristic position data.

9. A power lithium battery operating temperature control system, characterized in that: The system comprises a memory and a processor, wherein the memory includes a program of a method for controlling the operating temperature of a power lithium battery. When the program of the method for controlling the operating temperature of a power lithium battery is executed by the processor, the following steps are implemented: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data, and process them to obtain the reaction enthalpy change matrix; Acquire the acoustic signal of the power lithium battery and process it to obtain the lithium dendrite signal amplitude data; Training the initial lithium battery heat generation power prediction model to obtain a trained lithium battery heat generation power prediction optimization model; The measured lithium dendrite signal amplitude data is used to obtain the measured reaction enthalpy change matrix, measured acoustic signal and real-time operation recording data of the power lithium battery, and the data is input into the lithium battery heat generation power prediction optimization model for processing to obtain the real-time prediction value of the heat generation power of the power lithium battery; Obtaining heat dissipation capacity data of the power lithium battery and comparing it with the real-time predicted value of heat generation power to obtain a heat dissipation under-ratio; The heat dissipation under-rate is compared with a preset heat dissipation under-rate warning threshold, and a temperature control strategy is executed according to the threshold comparison result.

10. The power lithium battery operating temperature control system according to claim 9, characterized in that: The step of obtaining the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data and processing them to obtain a reaction enthalpy change matrix includes: Obtain the state of charge data of the power lithium battery and the corresponding reaction enthalpy change evaluation data; The reaction enthalpy change evaluation data includes initial state energy, delithiation state energy and lithium ion chemical potential; Processing is performed according to the initial state energy, the delithiation state energy and the lithium ion chemical potential to obtain reaction enthalpy change data corresponding to the state of charge data; The state of charge data and the corresponding reaction enthalpy change data are processed to obtain a reaction enthalpy change matrix.

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