High-precision internal heat production calculation method and system for power battery

Abstract

The application provides a high-precision power battery internal heat production calculation method and system, which calibrates key parameters affecting irreversible heat production and reversible heat production of the battery in real time and adaptively by using accurate battery internal feature temperature and integrating real-time operation data of the battery, and inputs the calibrated parameters into a refined electrochemical-heat production model, so as to realize high-precision and real-time dynamic calculation of total heat production and components in the battery. The application can significantly improve the accuracy and robustness of heat production calculation, and the superiority is verified by comparison with test results of an accelerated calorimeter, so that more accurate decision basis is provided for a battery thermal management system, the risk of thermal runaway is effectively avoided, and the service life of the battery is prolonged.

Description

Technical Field

[0001] This invention relates to the fields of lithium-ion battery thermal management, electrochemical modeling and state estimation technology, specifically to a high-precision method and system for calculating the internal heat generation of a power battery. Background Technology

[0002] With the increasing demands on lithium-ion battery performance from electric vehicles, energy storage systems, and other applications, the importance of battery thermal management is becoming increasingly prominent. Internal heat generation within the battery is the root cause of temperature increases, impacting battery performance, accelerating aging, and even triggering thermal runaway. Therefore, accurately and in real-time acquiring the internal heat generation of the battery is a crucial prerequisite for achieving efficient thermal management and ensuring safe battery operation.

[0003] Existing methods for calculating battery heat generation are typically based on electrochemical-thermal coupling models. These models decompose the total heat generation of a battery into irreversible heat generation (ohmic heat generation, polarization heat generation, etc.) and reversible heat generation (entropy heat). However, many key parameters in these heat generation models, such as internal ohmic resistance, electrochemical polarization overpotential, and entropy heat coefficient, are often treated as static constants in practical applications, or identified only through one-time offline experiments. This approach ignores the dynamic changes of these parameters under different operating conditions, different states of health (SOH), and different states of charge (SOC). For example, the battery's internal resistance and entropy heat coefficient vary significantly with temperature, SOC, and aging. In particular, reversible heat generation (… The calculation of the internal characteristic temperature of the battery. Closely related to the internal temperature distribution and dynamic changes of the battery, existing methods often use surface temperature or average temperature as substitutes, making it difficult to accurately capture the true temperature distribution and dynamic changes inside the battery, thus affecting the accuracy of heat generation calculations. Static parameters and inaccurate temperature inputs cause the accuracy of heat generation calculation results to drop significantly during actual operation, especially under extreme conditions such as high-rate charge / discharge, low temperature, or high temperature, failing to truly reflect the dynamic heat generation behavior of the battery. This poses a challenge to accurate thermal management.

[0004] Furthermore, since direct measurement of heat generation inside a battery is very difficult, the verification of heat generation models often relies on indirect temperature measurements or complex simulations. Therefore, there is a need for a method that can calculate the heat generation inside a battery in real time, dynamically, and with high accuracy, and can be verified through reliable experimental methods to overcome the limitations of existing technologies.

[0005] Patent application CN121189171A discloses a method and device for predicting battery heat generation. The method proposes first establishing a battery reaction kinetic model to identify internal electrochemical reaction kinetic parameters, then combining this with battery thermal conduction information to construct a multi-scale heat generation model. From this model, a multi-scale feature matrix containing operating condition parameters and heat generation parameters is constructed. Finally, machine learning is used to train the heat generation prediction model, achieving accurate prediction of battery heat generation based on operating condition parameters. The key feature of this approach is the integration of mechanistic and data-driven models: on the one hand, physical constraints are introduced through reaction kinetics and multi-scale thermal models to improve the physical interpretability of features; on the other hand, a learning model is used to explore the spatiotemporal correlation between operating condition parameters and heat generation parameters, improving the accuracy of heat generation prediction and its ability to adapt to complex operating conditions. However, this method relies on the accurate establishment of the reaction kinetic model and multi-scale thermal model, as well as the accurate acquisition of thermal conduction information and heat generation parameters. The initial modeling and parameter identification process is complex and costly. Furthermore, it requires a large amount of high-quality operating condition and heat generation calibration data for training. When there are significant differences in vehicle models or battery cells, remodeling or retraining may be necessary, posing challenges to engineering applications and large-scale deployment.

[0006] Patent document CN119936705B discloses a differential power-compensated battery isothermal calorimeter and its dynamic characteristic correction method. This method involves simultaneously arranging the battery under test and a reference battery of the same model in a constant-temperature heat sink, using flexible heating elements to control the power on both sides. The heating power on the reference side is used as a real-time baseline, and the real-time heat generation power during battery charging and discharging is obtained using the difference in heating power between the two sides. Dynamic characteristic correction is then achieved by combining piecewise polynomial fitting and time constant calibration, thereby improving the time response and measurement accuracy of isothermal calorimetry. The key feature of this method is the use of a differential power compensation structure, which effectively suppresses the influence of ambient temperature fluctuations on the calorimetric results. Furthermore, the joint correction of "power – power change rate – time constant" enhances the analytical capability for transient heat generation under rapid operating conditions. However, this method relies on a precise isothermal calorimetric cavity structure, symmetrical arrangement of dual batteries, and multi-channel temperature and power closed-loop control. It places high demands on the consistency calibration and operation and maintenance of the experimental setup, making it difficult to directly transfer to vehicle-mounted or conventional experimental environments. It is more suitable for high-precision thermal characteristic studies under laboratory conditions.

[0007] Patent document CN119780742B discloses an online method and system for estimating the internal temperature of lithium batteries. By constructing a sparse parameter model of the thermal path and embedding it as a physical constraint into a neural network and a temporal convolutional network that fuses physical information, it achieves high-precision internal temperature estimation relying only on a small amount of surface temperature, ambient temperature, and equivalent heat generation information. The computational load is relatively low, making it suitable for embedded edge deployment. This solution combines physical interpretability and data-driven generalization capabilities, but it still relies on prior data and experiments to complete thermal path parameter identification and internal temperature calibration. Different cell structures and packaging forms may require re-simplification of modeling and retraining, increasing the engineering cost for cross-platform applications.

[0008] Patent application CN119247189A proposes a battery health status assessment method based on thermal parameters. By acquiring the heat generation and specific heat capacity variation characteristics at the individual cell and system levels under different cycle numbers and temperatures, a baseline state function and a system function are constructed to assess and correct the battery system's health status, thereby improving the accuracy and efficiency of SOH assessment. This approach emphasizes the utilization of heat-capacity coupling characteristics, but it requires collecting complete thermal parameters and heat generation data under multiple temperature and cycle conditions. The initial calibration and experimental workload is substantial, which limits its application in large-scale in-service battery systems.

[0009] Patent application CN114695991A proposes a method for controlling the temperature of a power battery. This method acquires the cell temperature and target temperature in real time, combines the current heat generation of the battery with the actual heat exchange of the heat exchange device, calculates the remaining heat exchange demand, and uses this as a servo condition to dynamically control the heat exchange process, thereby reducing over-servoing and energy consumption of the thermal management system. This scheme incorporates battery heat generation into the closed-loop control of heat exchange, improving temperature control accuracy and energy efficiency. However, it still relies on the accuracy of cell temperature and heat generation estimation, placing high demands on the sensing and estimation models. The control effect may be affected when sensor layout is limited or estimation deviations are large. Summary of the Invention

[0010] To address the shortcomings of existing technologies, the purpose of this invention is to provide a high-precision method and system for calculating the internal heat generation of a power battery.

[0011] The high-precision method for calculating the internal heat generation of a power battery provided by the present invention includes: Step 1: Real-time acquisition of terminal voltage and charging / discharging current provided by the battery management system; acquisition of external or surface temperature of the battery through a non-invasive temperature sensor; simultaneous acquisition of real-time characteristic temperature of the battery's interior by integrating or calling an internal temperature estimation module based on a pre-embedded sensor and thermal resistance network; acquisition and estimation of the terminal voltage, charging / discharging current, external or surface temperature, and internal real-time characteristic temperature at a high sampling rate; preprocessing of the data using filtering techniques, including noise removal and time synchronization, and alignment of all data on the time axis. Step 2: Construct an online parameter identification module to receive the preprocessed data and use an adaptive algorithm to adaptively calibrate key parameters affecting battery heat generation in real time or periodically. These key parameters include the battery's internal ohmic resistance, electrochemical polarization overpotential, and entropy thermal coefficient. The battery's internal ohmic resistance is analyzed by the voltage drop of the battery's current pulse response, or tracked and updated online by combining characteristic frequency information from the electrochemical impedance spectroscopy. The electrochemical polarization overpotential is calibrated by the feedback error of the electrochemical model, or identified by the voltage-current dynamic response after separating the ohmic voltage drop. The entropy thermal coefficient is dynamically fine-tuned using a multidimensional lookup table based on state of charge, health state, and temperature, combined with the online parameter identification module, utilizing the real-time characteristic temperature inside the battery and the electrochemical model error. Step 3: Receive the calibrated key heat generation parameters output by the online parameter identification module and the real-time characteristic temperature inside the battery, and substitute them into the electrochemical heat generation model to calculate the irreversible heat generation, reversible heat generation, and total heat generation of the battery, which will be used as input to the battery thermal management system.

[0012] Preferably, the online parameter identification module employs an extended Kalman filter algorithm, whose state vector includes the internal ohmic resistance of the battery and the entropy-thermal coefficient, and whose measurement vector includes the terminal voltage and the surface temperature; the system dynamic equation and measurement equations Represented as:

[0013]

[0014] in, Let be the input current at time t-1. v(t) and v(t) are the process noise and measurement noise, respectively. Let be the state vector of the system at time t. It is the system's measurement vector at time t; The extended Kalman filter uses the system state transition function. and system observation function By using the covariance matrix to iteratively update and correct the state vector, dynamic tracking of parameters can be achieved.

[0015] Preferably, the irreversible heat generation The calculation formula is:

[0016] in, Open circuit voltage, For the ohmmeter after online calibration, This is the charging and discharging current. The terminal voltage is t, and the time unit is t; Taking further consideration of the overpotential of the positive and negative electrodes, the formula for calculating irreversible heat generation is as follows:

[0017] in, To activate the overpotential, These overpotential terms are concentration overpotentials, calculated using an electrochemical model and online-calibrated exchange current density parameters.

[0018] Preferably, the formula for calculating the reversible heat generation is:

[0019] in, This refers to the characteristic temperature inside the battery. The calibrated entropy-heat coefficient; The total heat production is: .

[0020] Preferably, the method further includes a verification step based on the accelerated calorimeter test results: the real-time voltage, current, and surface temperature of the battery recorded during the simulated actual operating conditions of the charge-discharge cycle in the accelerated calorimeter experiment, as well as the internal characteristic temperature obtained by the battery internal temperature estimation module, are used as inputs to calculate the predicted real-time total heat generation; the predicted real-time total heat generation is compared with the actual heat generation directly measured by the accelerated calorimeter, and the prediction accuracy is quantified by calculating the root mean square error and mean absolute error of the two.

[0021] The high-precision power battery internal heat generation calculation system provided by the present invention includes: Module M1: Real-time acquisition of terminal voltage and charging / discharging current provided by the battery management system; acquisition of external or surface temperature of the battery via a non-invasive temperature sensor; and acquisition of real-time characteristic temperature of the battery's interior by integrating or calling an internal temperature estimation module based on a pre-embedded sensor and thermal resistance network. The terminal voltage, charging / discharging current, external or surface temperature, and internal real-time characteristic temperature are acquired and estimated at a high sampling rate, and preprocessed using filtering techniques, including noise removal and time synchronization, to align all data on the time axis. Module M2: Constructs an online parameter identification module that receives the preprocessed data and uses an adaptive algorithm to adaptively calibrate key parameters affecting battery heat generation in real time or periodically. These key parameters include the battery's internal ohmic resistance, electrochemical polarization overpotential, and entropy thermal coefficient. The battery's internal ohmic resistance is analyzed by the voltage drop of the battery's current pulse response, or tracked and updated online by combining characteristic frequency information from the electrochemical impedance spectroscopy. The electrochemical polarization overpotential is calibrated by the feedback error of the electrochemical model, or identified by the voltage-current dynamic response after separating the ohmic voltage drop. The entropy thermal coefficient is dynamically fine-tuned using a multidimensional lookup table based on state of charge, health state, and temperature, combined with the online parameter identification module, utilizing the real-time characteristic temperature inside the battery and the electrochemical model error. Module M3: Receives the calibrated key heat generation parameters output by the online parameter identification module and the acquired real-time characteristic temperature inside the battery, and substitutes them into the electrochemical heat generation model to calculate the irreversible heat generation, reversible heat generation, and total heat generation of the battery, which serve as inputs to the battery thermal management system.

[0022] Preferably, the online parameter identification module employs an extended Kalman filter algorithm, whose state vector includes the internal ohmic resistance of the battery and the entropy-thermal coefficient, and whose measurement vector includes the terminal voltage and the surface temperature; the system dynamic equation and measurement equations Represented as:

[0023]

[0024] in, Let be the input current at time t-1. v(t) and v(t) are the process noise and measurement noise, respectively. Let be the state vector of the system at time t. It is the system's measurement vector at time t; The extended Kalman filter uses the system state transition function. and system observation function By using the covariance matrix to iteratively update and correct the state vector, dynamic tracking of parameters can be achieved.

[0025] Preferably, the irreversible heat generation The calculation formula is:

[0026] in, Open circuit voltage, For the ohmmeter after online calibration, This is the charging and discharging current. The terminal voltage is t, and the time unit is t; Taking further consideration of the overpotential of the positive and negative electrodes, the formula for calculating irreversible heat generation is as follows:

[0027] in, To activate the overpotential, These overpotential terms are concentration overpotentials, calculated using an electrochemical model and online-calibrated exchange current density parameters.

[0028] Preferably, the formula for calculating the reversible heat generation is:

[0029] in, This refers to the characteristic temperature inside the battery. The calibrated entropy-heat coefficient; The total heat production is: .

[0030] Preferably, the method further includes a verification module based on the accelerated calorimeter test results: the real-time voltage, current, and surface temperature of the battery recorded during the charge-discharge cycle simulating actual working conditions in the accelerated calorimeter experiment, as well as the internal characteristic temperature obtained by the battery internal temperature estimation module, are used as inputs to calculate the predicted real-time total heat generation; the predicted real-time total heat generation is compared with the actual heat generation directly measured by the accelerated calorimeter, and the prediction accuracy is quantified by calculating the root mean square error and mean absolute error of the two.

[0031] Compared with the prior art, the present invention has the following beneficial effects: (1) By introducing the precise internal characteristic temperature of the battery as input and combining it with online adaptive calibration of key heat generation parameters, this invention can reflect the dynamic characteristics of the battery under different operating conditions, SOC and SOH in real time, and greatly improve the accuracy of heat generation calculation. (2) By combining multi-source data fusion with adaptive calibration algorithm, this invention makes the method more robust to sensor noise and model uncertainty. The online calibration mechanism of heat generation parameters and real-time internal temperature input ensure the real-time nature of heat generation calculation results, which can capture the rapid changes in internal heat generation of the battery in a timely manner and provide an immediate and accurate decision basis for the thermal management system. (3) By comparing the results with those of the Accelerated Calorimeter (ARC) test, this invention proves the accuracy and reliability of the method in simulating the heat generation behavior of real batteries, thereby enhancing the practical value and credibility of the method. Attached Figure Description

[0032] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 Flowchart of a high-precision method for calculating internal heat generation in a power battery; Figure 2 A schematic diagram of the online parameter identification module; Figure 3 This is a comparison chart of verification results based on ARC testing. Detailed Implementation

[0033] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0034] Example 1 The purpose of this invention is to provide a high-precision method for calculating the internal heat generation of a power battery, so as to solve the problems of static parameters, inaccurate internal temperature input, and lack of reliable verification methods in existing heat generation calculations.

[0035] like Figure 1 The present invention can be divided into the following parts: (1) First step: implanting thermocouple sensors into a large-capacity square battery sample; (2) Second step: verifying the consistency of the battery before and after implanting thermocouples based on statistical methods and the results of changes in electrochemical model parameters; (3) Third step: establishing an electrochemical-thermal coupling model of a large-capacity square battery and obtaining the thermal resistance network characteristic parameters of the battery.

[0036] Step 1: Implanting thermocouple sensors into large-capacity square battery samples, mainly including: Multi-source operating data and internal temperature acquisition and preprocessing: Real-time acquisition of terminal voltage provided by the battery management system (BMS) and charging / discharging current Simultaneously, the external or surface temperature of the battery is collected using non-invasive temperature sensors (such as infrared sensors or surface-mount thermistors). More importantly, by integrating or calling an advanced battery internal temperature estimation module, such as a temperature estimation algorithm based on embedded sensors and thermal resistance networks, the real-time characteristic temperature inside the battery can be obtained. These data were collected and estimated at a high sampling rate, and then subjected to noise removal and time synchronization techniques such as moving average filtering and Kalman filtering to ensure that all data were aligned on the time axis.

[0037] Step 2: Online adaptive calibration of key heat generation parameters, which mainly includes the following parts: 1) Internal ohmic resistance

[0038] This parameter can be decomposed into the DC impedance of the positive and negative electrodes. and the DC resistance inside the battery ,Right now By analyzing the voltage drop of the battery current pulse response, or by combining the characteristic frequency information of electrochemical impedance spectroscopy (EIS), algorithms such as EKF or RLS can be used for online tracking and updating.

[0039] 2) Electrochemical polarization overpotential

[0040] This parameter is the driving force of the electrochemical reaction. It can be calibrated using the feedback error of the electrochemical model or identified through the voltage-current dynamic response after separating the ohmic voltage drop. The battery data after the thermocouple is implanted is cleaned by aligning the timestamp portions of the data acquired by the temperature acquisition device and the battery testing device according to the peak alignment principle.

[0041] 3) Entropy-heat coefficient

[0042] This parameter, describing the rate of change of open-circuit voltage with temperature, is a direct source of reversible heat generation. This coefficient typically varies significantly at different SOCs and may also change with SOH and temperature. This invention establishes a multidimensional lookup table based on SOC, SOH, and temperature, and combines it with an online calibration module, utilizing the characteristic temperature inside the battery. To compensate for errors in the electrochemical model, the coefficients in the lookup table are dynamically fine-tuned.

[0043] 4) Online parameter identification model (taking extended Kalman filter as an example) Assuming the battery state vector Include and Waiting for calibration parameters, measurement vector for The system dynamic equations and measurement equations can be expressed as:

[0044]

[0045] in, For input (such as) ), and These are process noise and measurement noise, respectively. EKF achieves dynamic parameter tracking by linearizing the f and h functions and using the covariance matrix to iteratively update and correct the state vector.

[0046] Step 3: Calculation of Refined Dynamic Heat Generation 1) The key parameters obtained from the calibration and the acquired data Substitute into the following heat production model to calculate the irreversible heat production. Reversible heat generation and total heat production .

[0047] 2) Irreversible heat generation : ,in, This is the battery's open-circuit voltage, determined by the state of charge (SOC) and obtained through a dynamically calibrated lookup table. This is the ohmic resistance after online calibration. More precisely, consider the overpotential at the positive and negative terminals: ,in, To activate the overpotential, These overpotential terms, which are concentration overpotentials, can be calculated using electrochemical models (such as the Butler-Volmer equation) and parameters such as online-calibrated exchange current density.

[0048] 3) Reversible heat generation : ,in, This refers to the characteristic temperature inside the battery. To calibrate the obtained entropy-thermal coefficient, a precise [mechanism / mechanism] is introduced. It significantly improves the accuracy of reversible heat generation calculation.

[0049] 4) Total heat production :

[0050] Example 2 The present invention also provides a system for implementing the above-mentioned high-precision method for calculating the internal heat generation of a power battery, the system comprising: 1) Data Acquisition and Internal Temperature Estimation Module: Responsible for real-time acquisition of battery voltage, current, and surface temperature data, and integrates an internal battery temperature estimation submodule to obtain... Data preprocessing module: performs filtering, synchronization, and outlier handling on the collected and estimated data.

[0051] 2) Online parameter identification module: such as Figure 2 Receive preprocessed data, run adaptive algorithms such as EKF or RLS, and then... , , Real-time calibration of key heat generation parameters is performed. This module is typically integrated into a BMS or a standalone embedded processor. Heat generation calculation module: receives dynamic parameters output from the online parameter identification module and... And calculate based on the above heat generation model , and .

[0052] 3) Data Output Module: This module outputs the calculation results to an external system. This system can be implemented using a high-performance microcontroller, digital signal processor (DSP), or field-programmable gate array (FPGA) to meet real-time computing and high-precision requirements. Integration with the BMS allows for full utilization of existing battery data and provides the BMS with more intelligent thermal management decision support.

[0053] To verify the accuracy and reliability of the high-precision method for calculating the internal heat generation of a power battery as described in this invention, an accelerated rate calorimeter (ARC) was used for experimental verification. ARC is a high-precision experimental device for measuring the self-heating or heat generation rate of a battery; its unique adiabatic environment can accurately capture the actual heat generation of the battery under different operating conditions.

[0054] Experimental setup: The battery under test (e.g., a high-capacity square lithium-ion battery) is placed in the ARC chamber. During the ARC test, the charging and discharging current and voltage of the battery are precisely controlled. Simultaneously, the ARC device can measure the rate of temperature rise of the battery under adiabatic or quasi-adiabatic conditions in real time with high precision, thereby calculating the battery's real-time heat generation power (i.e., the actual heat generation). ).

[0055] Data Acquisition: In the ARC experiment, the battery underwent a series of charge-discharge cycles simulating real-world operating conditions (e.g., pulse charge-discharge or continuous charge-discharge under different C rates and temperature conditions). During this process, the real-time battery voltage was recorded. Current Surface temperature And the heat generation power directly measured by ARC Furthermore, the internal temperature of the battery is obtained using a battery internal temperature estimation module integrated with the method of this invention. .

[0056] Verification process: Recording data from the ARC experiment , and the temperature obtained through the internal temperature estimation module As input to the method of this invention, firstly, the online parameter identification module (e.g., an EKF-based algorithm) in the method of this invention is used to... and Key heat generation parameters are calibrated in real time. Then, the calibrated parameters and... Substituting the values ​​into the refined dynamic heat production calculation module, the real-time total heat production predicted by this method is calculated. Finally, Compared with the actual heat production measured by the ARC experiment A comparison is made. The prediction accuracy of the method of the present invention is quantified by calculating the root mean square error (RMSE), mean absolute error (MAE), or relative error of the two.

[0057] Verification results: such as Figure 3 Experimental results show that the method described in this invention calculates... Compared with ARC measurement The results show a high degree of consistency, with errors significantly lower than methods using static parameters or those that do not consider dynamic changes in internal temperature. The predictive accuracy advantage of the method in this invention is even more pronounced under conditions of high-rate charging and discharging or drastic temperature changes.

[0058] Those skilled in the art will understand that, in addition to implementing the system, apparatus, and their modules provided by this invention in purely computer-readable program code, the same program can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system, apparatus, and their modules provided by this invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; alternatively, modules for implementing various functions can be considered both software programs implementing the method and structures within the hardware component.

[0059] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A high-precision method for calculating the internal heat generation of a power battery, characterized in that, include: Step 1: Real-time acquisition of terminal voltage and charging / discharging current provided by the battery management system; acquisition of external or surface temperature of the battery through a non-invasive temperature sensor; and acquisition of real-time characteristic temperature inside the battery by integrating or calling a battery internal temperature estimation module based on a pre-embedded sensor and thermal resistance network. The terminal voltage, charging and discharging current, external or surface temperature and internal real-time characteristic temperature are acquired and estimated at a high sampling rate, and preprocessed by filtering techniques, including noise removal and time synchronization, to align all data on the time axis. Step 2: Construct an online parameter identification module to receive the preprocessed data and use an adaptive algorithm to adaptively calibrate key parameters affecting battery heat generation in real time or periodically. These key parameters include the battery's internal ohmic resistance, electrochemical polarization overpotential, and entropy thermal coefficient. The battery's internal ohmic resistance is analyzed by the voltage drop of the battery's current pulse response, or tracked and updated online by combining characteristic frequency information from the electrochemical impedance spectroscopy. The electrochemical polarization overpotential is calibrated by the feedback error of the electrochemical model, or identified by the voltage-current dynamic response after separating the ohmic voltage drop. The entropy thermal coefficient is dynamically fine-tuned using a multidimensional lookup table based on state of charge, health state, and temperature, combined with the online parameter identification module, utilizing the real-time characteristic temperature inside the battery and the electrochemical model error. Step 3: Receive the calibrated key heat generation parameters output by the online parameter identification module and the real-time characteristic temperature inside the battery, and substitute them into the electrochemical heat generation model to calculate the irreversible heat generation, reversible heat generation, and total heat generation of the battery, which will be used as input to the battery thermal management system.

2. The high-precision method for calculating the internal heat generation of a power battery according to claim 1, characterized in that, The online parameter identification module employs an extended Kalman filter algorithm. Its state vector includes the internal ohmic resistance of the battery and the entropy-thermal coefficient, while the measurement vector includes the terminal voltage and the surface temperature. The system dynamic equation is as follows: and measurement equations Represented as: in, Let be the input current at time t-1. v(t) and v(t) are the process noise and measurement noise, respectively. Let be the state vector of the system at time t. It is the system's measurement vector at time t; The extended Kalman filter uses the system state transition function. and system observation function By using the covariance matrix to iteratively update and correct the state vector, dynamic tracking of parameters can be achieved.

3. The high-precision method for calculating the internal heat generation of a power battery according to claim 1, characterized in that, The irreversible heat generation The calculation formula is: in, Open circuit voltage, For the ohmmeter after online calibration, This is the charging and discharging current. The terminal voltage is t, and the time unit is t. Taking further consideration of the overpotential of the positive and negative electrodes, the formula for calculating irreversible heat generation is as follows: in, To activate the overpotential, These overpotential terms are concentration overpotentials, calculated using an electrochemical model and online-calibrated exchange current density parameters.

4. The high-precision method for calculating the internal heat generation of a power battery according to claim 3, characterized in that, The formula for calculating the reversible heat generation is as follows: in, This refers to the characteristic temperature inside the battery. The calibrated entropy-heat coefficient; The total heat production is: .

5. The high-precision method for calculating the internal heat generation of a power battery according to claim 1, characterized in that, It also includes a verification step based on the accelerated calorimeter test results: the real-time voltage, current, and surface temperature of the battery recorded during the simulated actual operating conditions of the charge-discharge cycle in the accelerated calorimeter experiment, as well as the internal characteristic temperature obtained by the battery internal temperature estimation module, are used as inputs to the method to calculate the predicted real-time total heat generation; the predicted real-time total heat generation is compared with the actual heat generation directly measured by the accelerated calorimeter, and the prediction accuracy is quantified by calculating the root mean square error and mean absolute error of the two.

6. A high-precision power battery internal heat generation calculation system, characterized in that, include: Module M1: Real-time acquisition of terminal voltage and charging / discharging current provided by the battery management system; acquisition of external or surface temperature of the battery through a non-invasive temperature sensor; and acquisition of real-time characteristic temperature inside the battery by integrating or calling an internal temperature estimation module based on a pre-embedded sensor and thermal resistance network. The terminal voltage, charging and discharging current, external or surface temperature and internal real-time characteristic temperature are acquired and estimated at a high sampling rate, and preprocessed by filtering techniques, including noise removal and time synchronization, to align all data on the time axis. Module M2: Constructs an online parameter identification module that receives the preprocessed data and uses an adaptive algorithm to adaptively calibrate key parameters affecting battery heat generation in real time or periodically. These key parameters include the battery's internal ohmic resistance, electrochemical polarization overpotential, and entropy thermal coefficient. The battery's internal ohmic resistance is analyzed by the voltage drop of the battery's current pulse response, or tracked and updated online by combining characteristic frequency information from the electrochemical impedance spectroscopy. The electrochemical polarization overpotential is calibrated by the feedback error of the electrochemical model, or identified by the voltage-current dynamic response after separating the ohmic voltage drop. The entropy thermal coefficient is dynamically fine-tuned using a multidimensional lookup table based on state of charge, health state, and temperature, combined with the online parameter identification module, utilizing the real-time characteristic temperature inside the battery and the electrochemical model error. Module M3: Receives the calibrated key heat generation parameters output by the online parameter identification module and the acquired real-time characteristic temperature inside the battery, and substitutes them into the electrochemical heat generation model to calculate the irreversible heat generation, reversible heat generation, and total heat generation of the battery, which serve as inputs to the battery thermal management system.

7. The high-precision power battery internal heat generation calculation system according to claim 6, characterized in that, The online parameter identification module employs an extended Kalman filter algorithm. Its state vector includes the internal ohmic resistance of the battery and the entropy-thermal coefficient, while the measurement vector includes the terminal voltage and the surface temperature. The system dynamic equation is as follows: and measurement equations Represented as: in, Let be the input current at time t-1. v(t) and v(t) are the process noise and measurement noise, respectively. Let be the state vector of the system at time t. It is the system's measurement vector at time t; The extended Kalman filter uses the system state transition function. and system observation function By using the covariance matrix to iteratively update and correct the state vector, dynamic tracking of parameters can be achieved.

8. The high-precision power battery internal heat generation calculation system according to claim 6, characterized in that, The irreversible heat generation The calculation formula is: in, Open circuit voltage, For the ohmmeter after online calibration, This is the charging and discharging current. The terminal voltage is t, and the time unit is t. Taking further consideration of the overpotential of the positive and negative electrodes, the formula for calculating irreversible heat generation is as follows: in, To activate the overpotential, These overpotential terms are concentration overpotentials, calculated using an electrochemical model and online-calibrated exchange current density parameters.

9. The high-precision power battery internal heat generation calculation system according to claim 8, characterized in that, The formula for calculating the reversible heat generation is as follows: in, This refers to the characteristic temperature inside the battery. The calibrated entropy-heat coefficient; The total heat production is: .

10. The high-precision power battery internal heat generation calculation system according to claim 6, characterized in that, It also includes a verification module based on the test results of an accelerated calorimeter: the real-time voltage, current, and surface temperature of the battery recorded during the charge-discharge cycle simulating actual working conditions in the accelerated calorimeter experiment, as well as the internal characteristic temperature obtained through the battery internal temperature estimation module, are used as inputs to calculate the predicted real-time total heat generation; the predicted real-time total heat generation is compared with the actual heat generation directly measured by the accelerated calorimeter, and the prediction accuracy is quantified by calculating the root mean square error and mean absolute error of the two.

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