A sodium-ion battery cycle life prediction method fusing electrochemical-thermal coupling model

Abstract

The application discloses the technical field of sodium ion battery health state prediction, and particularly relates to a sodium ion battery cycle life prediction method fusing an electrochemical-thermal coupling model, which comprises the following steps: collecting basic parameters and working condition operation data of a target sodium ion battery, wherein the basic parameters include battery electrode thickness, electrolyte concentration, ion diffusion coefficient, pole piece porosity, specific heat capacity, thermal conductivity and heat exchange coefficient, and the working condition operation data includes charge-discharge rate, working current, working voltage, real-time surface temperature, environment temperature and cycle number; the application breaks through the technical limitation of a single model, constructs an electrochemical-thermal bidirectional closed loop coupling model, completely restores the real mechanism that electrothermal interaction and coupling aging occur in the charge-discharge process of the sodium ion battery, solves the problem that the traditional method separates the electrothermal correlation and the aging mechanism is not accurately described, and improves the accuracy of life prediction in principle.

Description

Technical Field

[0001] This invention relates to the field of sodium-ion battery health status prediction technology, specifically a method for predicting the cycle life of sodium-ion batteries by integrating an electrochemical-thermal coupling model. Background Technology

[0002] Sodium-ion batteries, with their advantages of abundant resources, low cost, excellent low-temperature performance, and high safety, have become core energy storage devices in large-scale energy storage, low-speed new energy vehicles, and distributed power supply. Battery cycle life is a core indicator for measuring the service performance and reliability of sodium-ion batteries. Accurately predicting the cycle life of a battery throughout its entire life cycle is of significant engineering importance for battery operation and maintenance scheduling, retirement prediction, and safety risk prevention and control.

[0003] During long-term charge-discharge cycles, sodium-ion batteries experience a dual degradation effect of electrochemical aging and thermal aging. Electrochemically, there are aging phenomena such as irreversible insertion and extraction of sodium ions, electrolyte decomposition, loss of electrode active materials, and increased interfacial impedance. Thermally, there is battery temperature rise caused by heat generated during charge and discharge, uneven temperature gradient distribution, and high temperature accelerates side reactions, exacerbating electrochemical aging. Intensified electrochemical polarization further increases the heat generation rate. The two form a strongly coupled aging mechanism that directly determines the battery's cycle degradation rate and final service life.

[0004] Existing methods for predicting the cycle life of sodium-ion batteries mainly fall into two categories: data-driven methods and single-mechanism model methods. Data-driven methods rely on massive amounts of experimental data to train models, resulting in poor generalization and an inability to adapt to complex real-world operating conditions such as varying temperatures and rates. Furthermore, they struggle to reveal the internal aging mechanisms of the battery. Single-mechanism electrochemical models only consider the electrochemical aging characteristics of the electrodes and electrolytes, neglecting the accelerating effect of the temperature field on aging. Single-mechanism thermal models only analyze the battery's temperature rise characteristics, failing to characterize the essence of internal electrochemical degradation. All of these methods sever the bidirectional coupling between electrochemistry and thermal field, failing to accurately depict the actual aging process of sodium-ion batteries. They suffer from low prediction accuracy, poor adaptability to operating conditions, and weak mechanistic explanation, making it difficult to meet the needs of precise cycle life prediction in engineering applications. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems of existing sodium-ion battery life prediction methods, such as fragmented electrothermal coupling mechanism, low prediction accuracy, poor adaptability to operating conditions, and unclear mechanism. This invention provides a method for predicting the cycle life of sodium-ion batteries by integrating an electrochemical-thermal coupling model. By constructing a bidirectional coupled electrothermal mechanism model, the coupled aging effect is accurately quantified, and high-precision prediction of battery cycle life under complex operating conditions is achieved.

[0006] Therefore, the purpose of this invention is to provide a method for predicting the cycle life of sodium-ion batteries by incorporating an electrochemical-thermal coupling model.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] A method for predicting the cycle life of sodium-ion batteries by integrating an electrochemical-thermal coupling model includes: S1, collecting basic parameters and operating data of the target sodium-ion battery, wherein the basic parameters include battery electrode thickness, electrolyte concentration, ion diffusion coefficient, electrode porosity, specific heat capacity, thermal conductivity, and heat transfer coefficient, and the operating data includes charge / discharge rate, operating current, operating voltage, real-time surface temperature, ambient temperature, and number of cycles;

[0009] S2. Based on the aforementioned basic parameters, construct an electrochemical mechanism model for sodium-ion batteries, and based on the aforementioned thermal parameters, construct a three-dimensional transient heat conduction model. Establish a two-way coupling relationship between the electrochemical model and the thermal model to obtain an electrochemical-thermal coupling benchmark model.

[0010] S3. Based on the electrochemical-thermal coupling benchmark model, simulate the single charge-discharge cycle process of the battery, extract electrochemical aging characteristic parameters and thermal aging characteristic parameters, and quantify the aging law of battery capacity decay and internal resistance growth under the electrothermal coupling effect.

[0011] S4. Combining electrochemical aging and thermal aging laws, a multi-factor coupled sodium-ion battery cycle aging degradation model is constructed, and temperature correction coefficient and rate correction coefficient are introduced to adapt to variable operating conditions.

[0012] S5. The cyclic aging decay model is iteratively calibrated using measured cyclic aging data to eliminate theoretical errors in the model and obtain an optimized lifetime prediction model.

[0013] S6. Input the real-time operating condition data of the battery into the optimized life prediction model, iteratively calculate the remaining number of cycles of the battery, and complete the cycle life prediction of the sodium-ion battery.

[0014] As a preferred embodiment of the sodium-ion battery cycle life prediction method integrating an electrochemical-thermal coupling model as described in this invention, the sodium-ion battery electrochemical mechanism model in step S2 is a P2D electrochemical model based on porous electrode theory. The model's governing equations include ion diffusion equations, charge conservation equations, and electrochemical reaction kinetic equations, specifically used to characterize the dynamic processes of sodium ion insertion / extraction, electrode polarization, and electrolyte concentration changes during battery charging and discharging.

[0015] As a preferred embodiment of the sodium-ion battery cycle life prediction method integrating an electrochemical-thermal coupling model as described in this invention, the three-dimensional transient heat conduction model is built based on the battery heat transfer mechanism and can accurately simulate the dynamic process of heat generation, conduction and external heat dissipation inside the battery; the total heat generation of the battery consists of three parts: electrochemical reaction heat, Joule heat and polarization heat. The heat generation rate is synchronized to the electrochemical model in real time, and the internal resistance, polarization voltage and other parameters output by the electrochemical model are dynamically updated to update the heat generation parameters of the thermal model, realizing real-time bidirectional closed-loop coupling and linkage between the electrochemical field and the temperature field.

[0016] As a preferred embodiment of the sodium-ion battery cycle life prediction method integrating an electrochemical-thermal coupling model as described in this invention, the electrochemical aging characteristic parameters in step S3 include the solid-phase diffusion coefficient decay rate, charge transfer impedance growth rate, electrolyte decomposition rate, and active material loss rate; the thermal aging characteristic parameters include the maximum temperature rise, average temperature rise, temperature gradient, and high temperature duration.

[0017] As a preferred embodiment of the sodium-ion battery cycle life prediction method integrating an electrochemical-thermal coupling model described in this invention, the multi-factor coupled sodium-ion battery cycle aging degradation model in step S4 constructs a core degradation relationship based on the thermal aging degradation coefficient and the electrochemical aging degradation coefficient. Combined with the temperature coupling correction function and the rate coupling correction function, it can dynamically correct the battery health state degradation value according to different cycle numbers, ambient temperature, and charge / discharge rate, accurately depicting the nonlinear aging degradation law of the battery under varying operating conditions.

[0018] As a preferred embodiment of the method for predicting the cycle life of sodium-ion batteries using an electrochemical-thermal coupling model as described in this invention, the parameter iterative calibration in step S5 specifically includes: comparing the measured capacity decay data and internal resistance change data of the battery with the model simulation data, using the least squares method to iteratively optimize the aging decay coefficient, temperature correction coefficient, and rate correction coefficient, continuously reducing the deviation between the simulation data and the measured data, and completing the model parameter optimization calibration when the data error between the two reaches a preset minimum threshold.

[0019] As a preferred embodiment of the sodium-ion battery cycle life prediction method integrating an electrochemical-thermal coupling model as described in this invention, the specific method of cycle life prediction in step S6 is as follows: taking the battery capacity decay to 80% of the rated capacity as the life end judgment threshold, continuously iterating the capacity decay trend under different operating conditions through the optimized model, and fitting to obtain the total cycle life and the remaining number of cycles of the battery.

[0020] As a preferred embodiment of the sodium-ion battery cycle life prediction method integrating an electrochemical-thermal coupling model as described in this invention, the method includes: a data acquisition module, a model construction module, a feature extraction module, a model calibration module, and a life prediction module.

[0021] The data acquisition module is used to collect basic parameters and real-time operating data of sodium-ion batteries.

[0022] The model building module is used to build an electrochemical mechanism model, a three-dimensional transient thermal model, and a bidirectional coupling benchmark model of the two.

[0023] The feature extraction module is used to extract electrothermal coupling aging feature parameters and quantify the battery aging degradation law.

[0024] The model calibration module is used to iteratively optimize the parameters of the aging prediction model based on measured data.

[0025] The life prediction module is used to input real-time operating condition data and output the remaining cycle life of the battery.

[0026] Compared with existing technologies:

[0027] 1. This invention breaks through the technical limitations of a single model and constructs an electrochemical-thermal two-way closed-loop coupling model, which fully restores the real mechanism of mutual promotion of electrothermal and coupled aging during the charging and discharging process of sodium-ion batteries. It solves the problems of traditional methods that sever the electrothermal correlation and fail to accurately characterize the aging mechanism, thus improving the accuracy of lifetime prediction in principle.

[0028] 2. This invention introduces a multi-factor correction mechanism, which can adapt to complex actual working conditions such as variable temperature, variable rate, and variable load. Compared with the traditional fixed working condition prediction method, the working condition generalization ability is greatly improved, and it can simultaneously meet the life prediction needs of multiple scenarios such as laboratory aging test, energy storage power station, and vehicle operation.

[0029] 3. This invention combines mechanistic modeling and data calibration, taking into account both the mechanistic interpretability and prediction accuracy of the model. It does not require massive training data, has high modeling efficiency, and the prediction error is stably controlled within 3%, which is far superior to traditional data-driven models and single-mechanism models, and has extremely strong engineering applicability.

[0030] 4. This invention can realize dynamic life prediction of the entire battery life cycle, update the battery aging status in real time, accurately predict the battery retirement node, and provide reliable technical support for intelligent operation and maintenance, life management and safety early warning of sodium-ion battery systems. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0032] This invention provides a method for predicting the cycle life of sodium-ion batteries using an electrochemical-thermal coupling model, comprising the following steps:

[0033] S1. Collect the basic parameters and operating data of the target sodium-ion battery. The basic parameters include battery electrode thickness, electrolyte concentration, ion diffusion coefficient, electrode porosity, specific heat capacity, thermal conductivity, and heat transfer coefficient. The operating data includes charge / discharge rate, operating current, operating voltage, real-time surface temperature, ambient temperature, and number of cycles.

[0034] S2. Based on the aforementioned basic parameters, construct an electrochemical mechanism model for sodium-ion batteries, and based on the aforementioned thermal parameters, construct a three-dimensional transient heat conduction model. Establish a two-way coupling relationship between the electrochemical model and the thermal model to obtain an electrochemical-thermal coupling benchmark model.

[0035] S3. Based on the electrochemical-thermal coupling benchmark model, simulate the single charge-discharge cycle process of the battery, extract electrochemical aging characteristic parameters and thermal aging characteristic parameters, and quantify the aging law of battery capacity decay and internal resistance growth under the electrothermal coupling effect.

[0036] S4. Combining electrochemical aging and thermal aging laws, a multi-factor coupled sodium-ion battery cycle aging degradation model is constructed, and temperature correction coefficient and rate correction coefficient are introduced to adapt to variable operating conditions.

[0037] S5. The cyclic aging decay model is iteratively calibrated using measured cyclic aging data to eliminate theoretical errors in the model and obtain an optimized lifetime prediction model.

[0038] S6. Input the real-time operating condition data of the battery into the optimized life prediction model, iteratively calculate the remaining number of cycles of the battery, and complete the cycle life prediction of the sodium-ion battery.

[0039] Step 1: Data acquisition and determination of the target sodium-ion battery's basic parameters: positive electrode thickness 80μm, negative electrode thickness 60μm, electrode porosity 0.35, electrolyte concentration 1.2mol / L, solid-phase sodium ion diffusion performance, battery equivalent specific heat capacity, equivalent thermal conductivity, and surface heat transfer coefficient; acquisition of measured data on charge and discharge voltage, current, surface temperature, cycle number, and capacity decay under variable-rate conditions of 0.5C-2C and 25℃-45℃.

[0040] The sodium-ion battery electrochemical mechanism model described in step S2 is a P2D electrochemical model based on porous electrode theory. The model's governing equations include ion diffusion equations, charge conservation equations, and electrochemical reaction kinetic equations. Specifically, it is used to characterize the dynamic processes of sodium ion insertion / extraction, electrode polarization, and electrolyte concentration changes during battery charging and discharging.

[0041] Step 2: Construct an electrochemical-thermal coupled benchmark model. Based on the P2D porous electrode theory, build an electrochemical model and solve the equations for sodium ion solid-phase diffusion, liquid-phase mass transfer, charge conservation, and electrochemical reaction kinetics. Based on the three-dimensional transient heat transfer equation, construct a thermal model and calculate the total heat generated by electrochemical reaction heat, Joule heat, polarization heat, and so on. Establish a two-way coupling correlation and complete parameter interaction updates every 0.1s to achieve real-time linkage between the temperature field and the electrochemical state.

[0042] The three-dimensional transient heat conduction model is built based on the battery heat transfer mechanism and can accurately simulate the dynamic process of heat generation, conduction and external heat dissipation inside the battery. The total heat generation of the battery consists of three parts: electrochemical reaction heat, Joule heat and polarization heat. The heat generation rate is synchronized to the electrochemical model in real time. At the same time, the internal resistance, polarization voltage and other parameters output by the electrochemical model dynamically update the heat generation parameters of the thermal model, realizing real-time two-way closed-loop coupling and linkage between the electrochemical field and the temperature field.

[0043] The electrochemical aging characteristic parameters mentioned in step S3 include the solid-phase diffusion coefficient decay rate, charge transfer impedance growth rate, electrolyte decomposition rate, and active material loss rate; the thermal aging characteristic parameters include the maximum temperature rise, average temperature rise, temperature gradient, and high temperature duration.

[0044] Step 3: Conduct coupled simulation experiments to simulate the multi-cycle charge and discharge process of the battery and extract aging characteristic parameters: thermal aging characteristics include average temperature rise per cycle, duration of high temperature, and temperature gradient; electrochemical aging characteristics include charge transfer impedance growth rate, active material loss rate, and electrolyte decomposition rate. Quantitatively, under the conditions of 40℃ high temperature and 2C high rate, the electrothermal coupled aging decay rate is 2.3 times that under the conditions of normal temperature and low rate.

[0045] The multi-factor coupled sodium-ion battery cycle aging degradation model described in step S4 is based on the thermal aging degradation coefficient and the electrochemical aging degradation coefficient to construct the core degradation relationship. Combined with the temperature coupling correction function and the rate coupling correction function, it can dynamically correct the battery health state degradation value according to different cycle numbers, ambient temperature and charge / discharge rate, and accurately characterize the nonlinear aging degradation law of the battery under different operating conditions.

[0046] Step 4: Construct a multi-factor coupled aging degradation model, calibrate the thermal aging degradation coefficient and electrochemical aging degradation coefficient suitable for this battery, build a correction function suitable for different temperatures and charge / discharge rates, and construct a battery health state degradation model that can adapt to varying operating conditions.

[0047] The parameter iterative calibration in step S5 specifically includes: comparing the measured battery capacity decay data and internal resistance change data with the model simulation data, using the least squares method to iteratively optimize the aging decay coefficient, temperature correction coefficient, and rate correction coefficient, continuously reducing the deviation between the simulation data and the measured data, and completing the model parameter optimization calibration when the data error between the two reaches a preset minimum threshold.

[0048] Step 5: Model iterative calibration. Using 1000 cycles of measured aging data, the model parameters are iteratively optimized using the least squares method. The mean square error threshold for the termination of the iteration is set to 10⁻⁴. After calibration, the deviation between the simulated capacity data and the measured data is less than 2.8%.

[0049] The specific method for predicting cycle life in step S6 is as follows: taking the battery capacity decay to 80% of the rated capacity as the life end judgment threshold, continuously iterating the capacity decay trend under different working conditions through the optimized model, and fitting the total cycle life and remaining number of cycles of the battery.

[0050] Step 6: Lifetime prediction. Input the actual operating data of the battery under varying operating conditions into the optimization model and continuously iterate to calculate the capacity decay trend. When the capacity decays to 80% of the rated capacity, the battery life is determined to end. The predicted total cycle life of the sodium-ion battery under normal operating conditions is 1820 cycles. Compared with the measured life of 1852 cycles, the prediction error is only 1.7%, which shows excellent prediction accuracy.

[0051] It includes a data acquisition module, a model building module, a feature extraction module, a model calibration module, and a lifetime prediction module;

[0052] The data acquisition module is used to collect basic parameters and real-time operating data of sodium-ion batteries.

[0053] The model building module is used to build an electrochemical mechanism model, a three-dimensional transient thermal model, and a bidirectional coupling benchmark model of the two.

[0054] The feature extraction module is used to extract electrothermal coupling aging feature parameters and quantify the battery aging degradation law.

[0055] The model calibration module is used to iteratively optimize the parameters of the aging prediction model based on measured data.

[0056] The life prediction module is used to input real-time operating condition data and output the remaining cycle life of the battery.

[0057] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for predicting the cycle life of sodium-ion batteries using an integrated electrochemical-thermal coupling model, characterized in that, Includes the following steps: S1. Collect the basic parameters and operating data of the target sodium-ion battery. The basic parameters include battery electrode thickness, electrolyte concentration, ion diffusion coefficient, electrode porosity, specific heat capacity, thermal conductivity, and heat transfer coefficient. The operating data includes charge / discharge rate, operating current, operating voltage, real-time surface temperature, ambient temperature, and number of cycles. S2. Based on the aforementioned basic parameters, construct an electrochemical mechanism model for sodium-ion batteries, and based on the aforementioned thermal parameters, construct a three-dimensional transient heat conduction model. Establish a two-way coupling relationship between the electrochemical model and the thermal model to obtain an electrochemical-thermal coupling benchmark model. S3. Based on the electrochemical-thermal coupling benchmark model, simulate the single charge-discharge cycle process of the battery, extract electrochemical aging characteristic parameters and thermal aging characteristic parameters, and quantify the aging law of battery capacity decay and internal resistance growth under the electrothermal coupling effect. S4. Combining electrochemical aging and thermal aging laws, a multi-factor coupled sodium-ion battery cycle aging degradation model is constructed, and temperature correction coefficient and rate correction coefficient are introduced to adapt to variable operating conditions. S5. The cyclic aging decay model is iteratively calibrated using measured cyclic aging data to eliminate theoretical errors in the model and obtain an optimized lifetime prediction model. S6. Input the real-time operating condition data of the battery into the optimized life prediction model, iteratively calculate the remaining number of cycles of the battery, and complete the cycle life prediction of the sodium-ion battery.

2. The method for predicting the cycle life of sodium-ion batteries using an integrated electrochemical-thermal coupling model according to claim 1, characterized in that, The sodium-ion battery electrochemical mechanism model described in step S2 is a P2D electrochemical model based on porous electrode theory. The model's governing equations include ion diffusion equations, charge conservation equations, and electrochemical reaction kinetic equations. Specifically, it is used to characterize the dynamic processes of sodium ion insertion / extraction, electrode polarization, and electrolyte concentration changes during battery charging and discharging.

3. The method for predicting the cycle life of sodium-ion batteries based on an electrochemical-thermal coupling model according to claim 2, characterized in that, The three-dimensional transient heat conduction model is built based on the battery heat transfer mechanism and can accurately simulate the dynamic process of heat generation, conduction and external heat dissipation inside the battery. The total heat generation of the battery consists of three parts: electrochemical reaction heat, Joule heat and polarization heat. The heat generation rate is synchronized to the electrochemical model in real time. At the same time, the internal resistance, polarization voltage and other parameters output by the electrochemical model dynamically update the heat generation parameters of the thermal model, realizing real-time two-way closed-loop coupling and linkage between the electrochemical field and the temperature field.

4. The method for predicting the cycle life of sodium-ion batteries using an integrated electrochemical-thermal coupling model according to claim 1, characterized in that, The electrochemical aging characteristic parameters mentioned in step S3 include the solid-phase diffusion coefficient decay rate, charge transfer impedance growth rate, electrolyte decomposition rate, and active material loss rate; the thermal aging characteristic parameters include the maximum temperature rise, average temperature rise, temperature gradient, and high temperature duration.

5. The method for predicting the cycle life of a sodium-ion battery based on an electrochemical-thermal coupling model according to claim 4, characterized in that, The multi-factor coupled sodium-ion battery cycle aging degradation model described in step S4 constructs a core degradation relationship based on the thermal aging degradation coefficient and the electrochemical aging degradation coefficient. Combined with the temperature coupling correction function and the rate coupling correction function, it can dynamically correct the battery health state degradation value according to different cycle numbers, ambient temperature, and charge / discharge rate, accurately depicting the nonlinear aging degradation law of the battery under varying operating conditions.

6. The method for predicting the cycle life of a sodium-ion battery based on an electrochemical-thermal coupling model according to claim 1, characterized in that, The parameter iterative calibration in step S5 specifically includes: comparing the measured capacity decay data and internal resistance change data of the battery with the model simulation data, using the least squares method to iteratively optimize the aging decay coefficient, temperature correction coefficient, and rate correction coefficient, continuously reducing the deviation between the simulation data and the measured data, and completing the model parameter optimization calibration when the data error between the two reaches a preset minimum threshold.

7. The method for predicting the cycle life of a sodium-ion battery based on an electrochemical-thermal coupling model according to claim 1, characterized in that, The specific method for predicting cycle life in step S6 is as follows: taking the battery capacity decay to 80% of the rated capacity as the life end judgment threshold, continuously iterating the capacity decay trend under different operating conditions through the optimized model, and fitting the total cycle life and remaining number of cycles of the battery.

8. A device for predicting the cycle life of a sodium-ion battery, characterized in that, It includes a data acquisition module, a model building module, a feature extraction module, a model calibration module, and a lifetime prediction module; The data acquisition module is used to collect basic parameters and real-time operating data of sodium-ion batteries. The model building module is used to build an electrochemical mechanism model, a three-dimensional transient thermal model, and a bidirectional coupling benchmark model of the two. The feature extraction module is used to extract electrothermal coupling aging feature parameters and quantify the battery aging degradation law. The model calibration module is used to iteratively optimize the parameters of the aging prediction model based on measured data. The life prediction module is used to input real-time operating condition data and output the remaining cycle life of the battery.