CN120428118B - Dynamic prediction method and system for SOE of lithium iron phosphate battery pack and life evaluation based on P2D model - Google Patents
Dynamic prediction method and system for SOE of lithium iron phosphate battery pack and life evaluation based on P2D modelInfo
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Abstract
The invention discloses a method and a system for SOE dynamic prediction and life assessment of a lithium iron phosphate battery pack by fusing a P2D model, and relates to the technical field of battery management. The method comprises the steps of generating an SOE dynamic prediction result of the battery pack by using an electrochemical-thermal-mechanical multi-physical field coupled P2D model based on historical operation data and real-time state parameters, analyzing the prediction result by combining a long-term performance attenuation mechanism, evaluating the residual life of the battery pack, and outputting an evaluation result. And the SOE prediction precision is improved, the performance change rule is comprehensively reflected by using a life evaluation model, and the running efficiency is remarkably improved and the service life is prolonged by real-time monitoring and optimization. The invention can meet the requirement of efficient and reliable battery management under complex working conditions.
Description
Technical Field
The invention belongs to the technical field of battery management and life assessment, and particularly relates to a method and a system for dynamic prediction and life assessment of SOE (solid oxide electronic) of a lithium iron phosphate battery pack fused with a P2D (peer-to-peer) model.
Background
With the wide application of lithium iron phosphate batteries in the energy storage field, the demands for performance prediction and life assessment of the lithium iron phosphate batteries are increasing. Accurate SOE (State of Energy) dynamic prediction and life assessment can not only improve the running efficiency of the battery pack, but also prolong the service life of the battery pack, thereby reducing the overall running cost. However, the existing technology for dynamic prediction and life assessment of SOE of lithium iron phosphate battery pack still has shortcomings in terms of model precision, dynamic response capability and multi-physical field coupling analysis, and is difficult to meet the high-efficiency management requirement under complex working conditions.
In the prior art, publication number CN119382299B discloses an intelligent regulation and control method and system for the action of a photovoltaic energy storage battery pack, and the patent realizes the fine regulation and control and parameter optimization of the single charging and discharging action by collecting the state data of each battery unit in the photovoltaic energy storage battery pack in real time and utilizing an intelligent body model to analyze the state data and evaluate the target predictability. However, the technical scheme mainly depends on an intelligent body model to perform state prediction, lacks deep modeling of a multi-physical field coupling process inside the battery, and particularly in SOE dynamic prediction of the lithium iron phosphate battery, fails to fully consider interaction of electrochemical reaction and thermodynamic behavior, and possibly leads to insufficient prediction accuracy. In addition, the scheme has less attention to battery life evaluation, and the performance decay law of the battery pack in long-term operation is difficult to comprehensively reflect.
Another patent with publication number CN118446684B discloses an optimization method of a chemical battery energy storage system based on artificial intelligence, which utilizes a machine learning algorithm to evaluate the state of a recovered battery, and combines active equalization and passive equalization technologies through deep learning and optimization algorithm to realize overall optimization configuration and dynamic charge and discharge strategy adjustment of a battery pack. However, the technical scheme focuses on the overall optimization management of the battery pack, SOE dynamic prediction accuracy of the single battery is limited, and an electrochemical model is not introduced to accurately describe the internal state of the battery. In addition, although the scheme can evaluate the residual service life of the battery cell, the scheme lacks in-depth analysis of a performance attenuation mechanism during long-term operation of the battery pack, and high-precision service life evaluation is difficult to realize.
The problems indicate that the existing SOE dynamic prediction and life assessment technology of the lithium iron phosphate battery pack still has certain defects in the aspects of multi-physical field coupling modeling, dynamic response capability, long-term performance attenuation analysis and the like. Therefore, the invention provides a system for dynamically predicting SOE and evaluating service life of a lithium iron phosphate battery pack fused with a P2D model, which aims to improve the precision of SOE dynamic prediction by introducing an electrochemical-thermal-mechanical multi-physical field coupled P2D model, and realize more accurate service life evaluation by combining long-term performance attenuation mechanism analysis, thereby meeting the requirements of efficient and reliable battery management under complex working conditions.
Disclosure of Invention
The invention aims to provide a method and a system for dynamic prediction and life assessment of a lithium iron phosphate battery SOE (solid state electronic) by fusing a P2D (peer-to-peer) model, which mainly solve the defects in the aspects of multi-physical field coupling modeling, dynamic response capability and long-term performance attenuation analysis in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
A method for dynamically predicting SOE and evaluating service life of a lithium iron phosphate battery pack fused with a P2D model comprises the following steps:
s1, acquiring basic operation data of a lithium iron phosphate battery pack through a sensor network arranged in the battery pack, and establishing a special communication link between the battery pack and monitoring equipment;
S2, uploading historical operation data, real-time state parameters and environment variables of the battery pack through a special communication link;
S3, generating SOE dynamic prediction results of the battery pack by using an electrochemical-thermal-mechanical multi-physical field coupled P2D model based on historical operation data and real-time state parameters;
and S4, analyzing the prediction result by combining a long-term performance decay mechanism, evaluating the residual life of the battery pack, and outputting the evaluation result.
In the step S1, the method for establishing the dedicated communication link is as follows:
s11, acquiring an identity identifier of monitoring equipment accessed into a battery pack;
S12, taking a central point of the battery pack as a circle center, and taking half of the maximum geometric dimension of the battery pack as a radius to define a monitoring area for communication coverage;
S13, judging whether a relay node exists in the monitoring area, if not, directly communicating the monitoring equipment with a battery pack main control unit to complete the establishment of a special communication link, and if so, entering step S14;
and S14, defining a subarea which comprises a relay node and is concentric with the monitoring area as a relay coverage area by taking the central point of the battery pack as a circle center, and completing establishment of a special communication link by the monitoring equipment through communication between the relay node and the battery pack main control unit.
In the step S3, the SOE dynamic prediction result is calculated by using an electrochemical-thermal-mechanical multi-physical field coupled P2D model, and the specific steps are as follows:
S31, collecting operation parameters of the lithium iron phosphate battery pack, including voltage, current, temperature, internal resistance and electrolyte concentration distribution;
S32, dividing the interior of the battery into a plurality of microcells, wherein each microcell comprises an anode region, a cathode region and a diaphragm region, constructing an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation, defining the electrochemical reaction rate, the heat conduction coefficient and the mechanical stress distribution of each region, and respectively describing the electrochemical reaction process, the heat transfer behavior and the mechanical deformation condition in the interior of the battery;
s33, performing discretization processing on an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation in the battery by using a finite difference method based on the collected operation parameter data;
S34, forming a P2D model of multi-physical field coupling inside the battery through coupling of electrochemical reaction rate, thermal conductivity and mechanical stress distribution;
"coupling" refers to the mutual independence, interaction and association relationship between different physical fields (electrochemical reaction, thermal conduction, mechanical stress). Specifically:
The electrochemical reaction rate (electrochemical processes involving charge transfer, ion transport, etc.), the thermal conductivity (heat generation and diffusion law) and the mechanical stress distribution (material deformation, structural stress, etc.) do not exist independently, but are affected by physical laws.
S35, inputting the discretized P2D model with multiple physical field couplings into a computing platform of a main control unit, and completing initialization of the P2D model;
the three equations of the electrochemical reaction equation, the thermal conduction equation and the mechanical balance equation form a coupling model, and the coupling model is a P2D model.
"Discretizing a model" refers to the process of converting a continuous mathematical model (e.g., partial differential equations describing a physical field, continuous space or time domain) into a discrete numerical model for numerical solution by a computer. The core is to describe approximately the original continuous physical field or mathematical relationship with a finite set of "discrete points" or "cells".
S36, defining initial state parameters of the battery pack, including SOC, SOH and current environment temperature;
S37, calculating the energy change rate of the battery pack under different working conditions based on the P2D model, and deducing a dynamic change curve of the SOE through the energy change rate;
s38, introducing a dynamic response algorithm, and correcting a dynamic change curve of the SOE by combining current and voltage data acquired in real time;
And S39, outputting the corrected SOE dynamic change curve to a main control unit for guiding charge and discharge management of the battery pack.
In the step S4, the battery life evaluation method includes the steps of:
S41, acquiring capacity attenuation data of the battery pack under different cycle times, wherein the capacity attenuation data comprise a capacity retention rate and an internal resistance change rate;
s42, firstly, collecting capacity attenuation data of the battery pack under different cycle times, including a capacity retention rate and an internal resistance change rate, analyzing a performance attenuation mechanism of the battery pack in long-term operation, and identifying key influence factors including electrochemical side reaction, thermal runaway risk and mechanical fatigue;
S43, defining life assessment indexes based on a performance decay mechanism, wherein the life assessment indexes comprise cycle life, calendar life and safety life;
S44, training the acquired attenuation data by using a machine learning algorithm to generate a life assessment model;
s45, embedding a life evaluation model into the life evaluation module for predicting the residual life of the battery pack.
The machine learning algorithm is a support vector machine or neural network algorithm and is used for carrying out nonlinear fitting and prediction on capacity fading data.
The monitoring device and the main control unit are in data transmission through a special communication link, the communication coverage area of the special communication link is an area taking the center point of the battery pack as the center and taking half of the maximum geometric dimension of the battery pack as the radius, the SOE dynamic prediction module receives the output result of the P2D model and generates the SOE dynamic prediction result of the battery pack according to the result, and the life assessment module is combined with a long-term performance attenuation mechanism to assess the residual life of the battery pack and output the assessment result.
When the finite difference method is used for discretizing an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation, a numerical calculation mode of fixed step length is adopted.
In S32, an electrochemical reaction equation, a thermal conduction equation, and a mechanical equilibrium equation are constructed:
The electrochemical reaction process is modeled by an electrochemical field, and comprises a lithium ion solid-phase diffusion equation, a liquid-phase transmission equation and a Butler-Volmer equation;
lithium ion solid phase diffusion equation:
,
wherein the boundary conditions include particle center symmetry:
,
surface reaction flux:
,
Wherein t represents time, c s is solid-phase lithium concentration, D s is solid-phase diffusion coefficient, j is electrode reaction current density, F is faraday constant, R is radial coordinate in a spherical coordinate system, and R s represents radius of solid-phase lithium particles;
The liquid phase transmission equation comprises a conservation equation and a charge conservation equation of the lithium ion concentration in the electrolyte and the potential coupling, wherein the conservation equation of the lithium ion concentration in the electrolyte and the potential coupling is as follows:
,
the conservation of charge equation is:
,
wherein, v is a vector differential operator, c e is the liquid-phase lithium concentration, ε e is the volume fraction of the electrolyte, For the effective liquid phase diffusion coefficient,Is the migration number of lithium ions and is used for preparing the lithium ion battery,Is of liquid phase potential, K eff ,The effective conductivity of liquid-phase lithium and the diffusion conductivity of liquid-phase lithium are obtained;
the Butler-Volmer equation first describes the electrode/electrolyte interface reaction kinetics:
,
Exchange current density j 0 expression:
,
The overpotential η is defined as:
,
Where a a 、αc is the anode and cathode transfer coefficient, U ocp is the open circuit potential, Is the surface concentration of the solid-phase lithium,R is a gas constant, T is absolute temperature, and k is an intrinsic reaction rate constant;
the heat transfer behavior is represented by thermodynamic field coupling and comprises an energy conservation equation and temperature sensitive parameter correction, wherein the total heat production rate Q of the energy conservation equation comprises:
Joule heat: ,
Reaction heat: ,
Polarized heat: ,
The heat conduction equation:
,
Wherein, the In order to achieve a material density of the material,In order to fix the specific heat capacity under pressure,Represents the volumetric heat capacity, lambda is the thermal conductivity,Is the effective conductivity of solid-phase lithium;
temperature sensitive parameter correction is represented by the temperature dependence of diffusion coefficient and conductivity:
,
Wherein D represents the diffusion coefficient, D (T) represents the diffusion coefficient at temperature T, Expressed at a reference temperatureThe lower diffusion coefficient, E a, is the activation energy, T ref is the reference temperature;
mechanical equilibrium equation based on the combination of solid phase diffusion equation and elastic mechanical equation to calculate the internal stress of the particle :
,
Wherein E is elastic modulus, c s is solid-phase lithium concentration, c 0 is solid-phase lithium initial concentration,Indicating the maximum intercalation concentration of solid-phase lithium,Is the relative amount of deformation of a material under a force or environmental change.
In S32, the electrochemical reaction rate, the thermal conductivity and the mechanical stress distribution of each region are defined as follows:
The electrochemical reaction rate adopts a Butler-Volmer equation to describe the reaction kinetics of an electrode/electrolyte interface;
The heat conduction coefficient is determined by a positive electrode region and a negative electrode region together with a diaphragm region, the positive electrode region consists of an active substance, a conductive agent, a binder and a pore electrolyte, and the equivalent thermal conductivity w eff is calculated by a mixed model:
,
Wherein mu i is the volume fraction of each component, and w i is the intrinsic thermal conductivity of the material;
the energy conservation equation defines the source of heat generation, the heat transfer equation describes the transfer and accumulation of heat, the heat transfer equation being the "heat transfer end", directly correlating the heat generation Q with the temperature distribution T inside the cell, reflecting how the heat is transferred by conduction and causing a temperature change. The temperature sensitive correction establishes a bi-directional coupling, and as the temperature T increases, the diffusion coefficient D and conductivity increase (due to the exp term increase), resulting in an increased electrochemical reaction rate (j increase), and a change in joule heat Qohm (due to the correlation of σeff, κeff with conductivity). The parameter change in turn affects the heat generation rate Q (e.g., an increase in j results in an increase in Qrxn and Qpol), which in turn alters the temperature profile T by thermal conduction equations.
Describing heat transfer behavior:
the heat transfer behavior is a dynamic process of heat generation-conduction-accumulation, and can be divided into the following three links:
1. Heat generation stage (energy conservation equation dominant)
When the battery works, the electrochemical reaction (reaction heat Qrxn), the current resistance (Joule heat Qohm) and the entropy change (polarized heat Qpol) generate heat simultaneously, and the total heat generation rate Q is overlapped by the three parts. For example:
During high-current discharge, joule heat Qohm is remarkably increased;
At low temperatures, the reaction rate decreases (j decreases), but polarized heat Qpol may dominate the heat generation at higher activation energies Ea.
2. Heat conduction phase (heat conduction equation dominant)
Heat generation Q causes local temperature rise, forming a temperature gradient. Heat is transferred by thermal conductionFrom a high temperature region (e.g., the center of the pole piece) to a low temperature region (e.g., the battery surface). For example:
When the internal thermal conductivity lambda of the battery is low (such as a diaphragm material), heat is difficult to diffuse, and local hot spots are easy to form;
In liquid-cooled battery, the cooling medium is increased by lowering the surface temperature Accelerating heat removal.
3. Parameter feedback stage (temperature sensitive parameter correction leading)
The temperature T change modifies the diffusion coefficient D and the conductivity by the arrhenius equation:
temperature rise- & gtD increase- & gtlithium ion diffusion acceleration- & gtreaction current j increase- & gtQrxn and Qpol increase- & gttemperature further rise (positive feedback);
Temperature decrease→d decrease→diffusion lag→increase in lithium ion concentration gradient at the electrode surface→increase in overpotential η→increase in Qrxn (local overheating at low temperatures may be exacerbated). The diaphragm region is dominated by material properties;
the mechanical stress distribution is determined by the positive electrode region, the negative electrode region and the diaphragm region, wherein the stress sources of the positive electrode region and the negative electrode region are that the volume of active particles is changed due to lithium ion intercalation/deintercalation to induce local stress, the stress sources of the diaphragm region are external pressure conduction and thermomechanical stress, the external pressure conduction is that the battery packaging pressure is transmitted to the diaphragm through a pole piece and the anti-compression performance of the battery packaging pressure needs to be considered, and the thermomechanical stress is that the thermal expansion coefficient difference of the diaphragm and an electrode is caused by temperature change to generate interfacial shear stress.
Further, the implementation scene of the system comprises management of a lithium iron phosphate battery pack in a power battery management system or a photovoltaic energy storage system of an electric automobile.
The system comprises a lithium iron phosphate battery pack, monitoring equipment, a main control unit, an SOE dynamic prediction module and a life assessment module, wherein the monitoring equipment is in communication connection with the lithium iron phosphate battery pack through a special communication link, the main control unit is connected with the monitoring equipment, the SOE dynamic prediction module is connected with the main control unit and is fused with the P2D model, and the life assessment module is connected with the main control unit.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention accurately describes the internal state of the lithium iron phosphate battery by introducing an electrochemical-thermal-mechanical multi-physical field coupled P2D model. The traditional P2D model only focuses on electrochemical processes, and the method innovatively fuses thermodynamic (temperature field) and mechanical (stress/strain) analysis to more comprehensively reflect the internal state change of the battery. The P2D model can detail the electrochemical reaction process, the heat conduction behavior and the mechanical stress distribution inside the battery, so that the precision of SOE dynamic prediction is improved. Meanwhile, the invention combines the long-term performance attenuation mechanism of the battery pack to construct a life evaluation model, and can comprehensively reflect the performance change rule of the battery pack under complex working conditions. In addition, through real-time monitoring and adjustment of the operation strategy of the optimized battery pack, the operation efficiency of the battery pack is remarkably improved and the service life of the battery pack is prolonged. The technical means of the invention is specific and definite, and can meet the requirement of efficient and reliable battery management under complex working conditions.
(2) According to the invention, a dynamic response algorithm is introduced in the SOE dynamic prediction process, the closed-loop prediction of SOE is realized through the dynamic response algorithm (such as data-driven model update or self-adaptive filtering), and the problem of error accumulation of the traditional model caused by parameter aging or working condition mutation is solved.
And correcting the SOE dynamic change curve calculated based on the P2D model by combining current and voltage data acquired in real time. Compared with the defects in the aspect of dynamic response in the prior art, the method can timely capture the real-time change of the battery in the running process, quickly adjust the prediction result, enable SOE dynamic prediction to be more fit with the actual running condition, remarkably enhance the dynamic response capability of the system, further provide powerful support for more efficient charge and discharge management of the battery, and improve the running efficiency of the battery.
(3) According to the invention, through collecting capacity attenuation data of the battery pack under different cycle times, the performance attenuation mechanism of the battery pack in long-term operation is deeply analyzed, key influencing factors such as electrochemical side reaction, thermal runaway risk and mechanical fatigue are identified, and life assessment indexes such as cycle life, calendar life and safety life are defined based on the key influencing factors. Compared with the prior art which lacks deep analysis of a long-term performance attenuation mechanism and is difficult to realize high-precision life assessment, the method can more comprehensively and accurately assess the residual life of the battery pack, provides important reference for full life cycle management of the battery pack, is beneficial to prolonging the service life of the battery pack and reduces the overall operation cost.
(4) And the SOE dynamic prediction is combined with the life evaluation, and the differential management (such as balanced charge and discharge and abnormal monomer pre-warning) of the battery pack is realized by analyzing the energy loss accumulation effect (such as cyclic aging and calendar aging). While conventional life assessment relies on a fixed threshold (e.g., cycle number), the method captures the decay mechanism (e.g., lithium dendrite growth, electrolyte decomposition) under actual conditions through a multi-physical field coupling model.
In summary, the multi-physical field coupling model comprehensively considers the cross influence of electrochemistry, heat and mechanics, avoids the limitation of a single physical field model, and remarkably improves SOE prediction accuracy (especially under the working conditions of high-rate charge and discharge and wide temperature range).
Through a dynamic correction algorithm, the model can be quickly adapted to working condition changes (such as sudden large current and temperature fluctuation), prediction delay is reduced, and the model is suitable for scenes with high real-time requirements such as automatic driving, energy storage power stations and the like.
SOE dynamic prediction and life assessment share multiple physical field model parameters, repeated modeling is avoided, meanwhile, aging factor disassembly (such as distinguishing cyclic aging and calendar aging contribution) is realized through energy loss analysis, and data support is provided for battery pack maintenance.
The charge-discharge strategy based on SOE can avoid deep charge-discharge (DOD), reduce the risk of electrode stress accumulation, inhibit the phenomenon of lithium dendrite, reduce the risk of thermal runaway when the temperature field is optimized, and improve the safety.
The model framework is suitable for lithium iron phosphate battery packs with different specifications, only the division density of the micro units and parameter calibration are required to be adjusted, and the engineering application potential is provided.
Drawings
Fig. 1 is a functional block diagram of the present invention.
Fig. 2 is a flow chart of a system implementation of the present invention.
Detailed Description
The invention will be further illustrated by the following description and examples, which include but are not limited to the following examples.
Example 1
As shown in fig. 1, the invention provides a system for dynamically predicting and evaluating the SOE of a lithium iron phosphate battery pack fused with a P2D model, which comprises the lithium iron phosphate battery pack, monitoring equipment, a main control unit, a SOE dynamic prediction module and a life evaluation module, wherein the monitoring equipment is in communication connection with the lithium iron phosphate battery pack through a special communication link, the main control unit is connected with the monitoring equipment, the SOE dynamic prediction module is connected with the main control unit and fused with the P2D model, and the life evaluation module is connected with the main control unit;
The lithium iron phosphate battery is a core component of the whole system for storing and releasing electric energy. The monitoring equipment is connected with the battery pack through a physical interface and is used for collecting basic operation data of the battery pack in real time, including parameters such as voltage, current, temperature and the like. The monitoring device also interacts data with the master control unit via a dedicated communication link. The method for establishing the special communication link comprises the steps of firstly obtaining an identity identifier of monitoring equipment connected with the battery pack, and then defining a monitoring area for communication coverage by taking the center point of the battery pack as a circle center and taking half of the maximum geometric dimension of the battery pack as a radius. If there is no relay node in the monitoring area (the relay node is usually used in wireless communication to forward signal and enlarge network coverage), the monitoring equipment directly communicates with the main control unit, if there is a relay node, a sub-area containing the relay node and concentric with the monitoring area is further defined as a relay coverage area, and the monitoring equipment completes the establishment of a communication link with the main control unit through the relay node. The design of such a communication link ensures the stability and real-time of data transmission. The method for judging whether the relay node exists in the monitoring area is various, for example, whether the area with abnormal stable signal intensity exists in the detection area or the protocol type in the analysis data packet exists in the detection area, and part of relay equipment can reserve a specific protocol header.
The main control unit receives historical operation data and real-time state parameters from the monitoring equipment, and transmits the data to the P2D model for processing. As shown in fig. 2, first, for the data acquisition process of the lithium iron phosphate battery, operating parameters including voltage, current, temperature, internal resistance, and electrolyte concentration distribution are acquired through a sensor network disposed in the battery. These operating parameters are transmitted to the computing platform of the master control unit for subsequent modeling and analysis. In the process of constructing the P2D model, the inside of the battery is divided into a plurality of microcells, each of which includes a positive electrode region, a negative electrode region, and a separator region. Ion conduction is realized between the positive electrode region and the negative electrode region through the diaphragm region, and the diaphragm region simultaneously plays a role in preventing electrons from directly flowing. In each microcell, electrochemical reaction rates, thermal conductivity coefficients, and mechanical stress distributions are defined, which describe the electrochemical reaction process, heat transfer behavior, and mechanical deformation conditions, respectively, inside the battery. Based on the collected operation parameters, discretizing an electrochemical reaction equation, a thermal conduction equation and a mechanical balance equation by adopting a finite difference method, so as to form a multi-physical field coupling model inside the battery. After the model is initialized through the computing platform, the multi-physical field coupling relation inside the battery can be accurately described.
In the SOE dynamic prediction module, initial state parameters of the battery pack are first defined, including SOC (State of Charge), SOH (State of Health) and the current ambient temperature. And calculating the energy change rate of the battery pack under different working conditions based on the established P2D model, and deducing a dynamic change curve of the SOE. In order to improve the prediction accuracy, a dynamic response algorithm is introduced, and the SOE dynamic change curve is corrected by combining current and voltage data acquired in real time. The corrected SOE dynamic change curve is output to a monitoring system for guiding charge and discharge management of the battery pack. In the process, the P2D model provides a basic support for SOE dynamic prediction, and the accuracy of SOE prediction results is ensured by accurately describing the changes of electrochemical reaction rate, thermal conductivity and mechanical stress distribution in the battery.
The lifetime assessment module is built based on capacity fade data for the battery pack at different cycles, including capacity retention and internal resistance change rates. By analyzing the performance decay mechanism in long-term operation, key factors affecting the service life of the battery, such as electrochemical side reactions, thermal runaway risks and mechanical fatigue, are identified. Based on these performance decay mechanisms, life assessment indicators such as cycle life, calendar life, and safety life are defined. Training the acquired attenuation data by using a machine learning algorithm to generate a life assessment model. The machine learning algorithm is a support vector machine or neural network algorithm for non-linear fitting and prediction of capacity fade data. The model is embedded in a life assessment module for predicting the remaining life of the battery pack. In the process, the life evaluation module is matched with the P2D model, the life evaluation module depends on the multi-physical field coupling information provided by the P2D model, and the P2D model optimizes the prediction capacity through feedback of the P2D model.
In practical application, the system can be deployed in a power battery management system of an electric automobile. The monitoring device collects voltage, current and temperature data of the lithium iron phosphate battery in real time, for example, as the vehicle is running, and transmits these data to the main control unit via a dedicated communication link. The main control unit inputs the data into the P2D model, the P2D model generates an SOE dynamic prediction result through multi-physical field coupling analysis, and the result is transmitted to the SOE dynamic prediction module. Meanwhile, the service life evaluation module evaluates the residual service life of the battery pack by combining a long-term performance decay mechanism, and feeds back an evaluation result to a vehicle control system so that a driver can know the battery state in time and take corresponding measures.
In an industrial energy storage scene, the system has wide application value as well. For example, in a photovoltaic energy storage system, a lithium iron phosphate battery pack needs to be charged and discharged frequently as an energy storage device. The monitoring device collects the operation data of the battery pack in real time and transmits the operation data to the main control unit through a special communication link. The main control unit utilizes the P2D model to dynamically predict the state of the battery pack, and evaluates the residual life of the battery pack by combining with the life evaluation module. The real-time monitoring and evaluating mechanism can effectively prolong the service life of the battery pack and improve the operation efficiency of the system.
The system realizes accurate modeling of the complex multi-physical field interaction process inside the battery by introducing an electrochemical-thermal-mechanical multi-physical field coupled P2D model. And data transmission is carried out between the monitoring equipment and the battery pack main control unit through a special communication link, so that the real-time performance and accuracy of data acquisition are ensured. The P2D model is combined with the historical operation data and the real-time state parameters to generate an SOE dynamic prediction result, and the simple and efficient structural design ensures that the model converges more quickly in the training process and the prediction precision is higher. Through deep analysis of a long-term performance attenuation mechanism of the battery pack, more accurate service life assessment is realized, and the high-efficiency and reliable battery management requirements under complex working conditions are met.
Example 2
In order to better understand and implement the present invention for those skilled in the art, a specific implementation method of the present invention is further described below in connection with a specific application scenario.
A method for dynamically predicting SOE and evaluating service life of a lithium iron phosphate battery pack fused with a P2D model comprises the following steps:
S1, basic operation data of the lithium iron phosphate battery pack is collected through a sensor (monitoring equipment) network arranged in the battery pack, a special communication link between the battery pack and the sensor of the monitoring equipment is established, the monitoring equipment is connected with the lithium iron phosphate battery pack through a physical interface, and the basic operation data of the battery pack, including parameters such as voltage, current, temperature, internal resistance, electrolyte concentration distribution and the like, are collected in real time. These data are transmitted to the master unit via a dedicated communication link.
The method for establishing the special communication link is as follows:
s11, acquiring an identity identifier of monitoring equipment accessed into a battery pack;
S12, taking a central point of the battery pack as a circle center, and taking half of the maximum geometric dimension of the battery pack as a radius to define a monitoring area for communication coverage;
S13, judging whether a relay node exists in the monitoring area, if not, directly communicating the monitoring equipment with a battery pack main control unit to complete the establishment of a special communication link, and if so, entering step S14;
and S14, defining a subarea which comprises a relay node and is concentric with the monitoring area as a relay coverage area by taking the central point of the battery pack as a circle center, and completing establishment of a special communication link by the monitoring equipment through communication between the relay node and the battery pack main control unit.
S2, uploading historical operation data, real-time state parameters and environment variables of the battery pack through a special communication link;
S3, generating SOE dynamic prediction results of the battery pack by using an electrochemical-thermal-mechanical multi-physical field coupled P2D model based on historical operation data and real-time state parameters;
The SOE dynamic prediction result is obtained by calculating a P2D model of electrochemical-thermal-mechanical multi-physical field coupling, and the method comprises the following specific steps:
S31, collecting operation parameters of the lithium iron phosphate battery pack, including voltage, current, temperature, internal resistance and electrolyte concentration distribution;
s32, dividing the interior of the battery into a plurality of microcells, wherein each microcell comprises an anode region, a cathode region and a diaphragm region, constructing an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation, defining the electrochemical reaction rate, the heat conduction coefficient and the mechanical stress distribution of each region, and respectively describing the electrochemical reaction process, the heat transfer behavior and the mechanical deformation condition in the interior of the battery;
s33, performing discretization processing on an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation in the battery by using a finite difference method based on the collected operation parameter data;
When the finite difference method is used for discretizing an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation, a numerical calculation mode of fixed step length is adopted.
S34, forming a P2D model of multi-physical field coupling inside the battery by coupling electrochemical reaction rate, thermal conductivity and mechanical stress distribution;
s35, inputting the discretized P2D model with multiple physical field couplings into a computing platform of a main control unit, and completing initialization of the P2D model;
S36, defining initial state parameters of the battery pack, including SOC, SOH and current environment temperature;
S37, calculating the energy change rate of the battery pack under different working conditions based on the P2D model, and deducing a dynamic change curve of the SOE through the energy change rate;
s38, introducing a dynamic response algorithm, and correcting a dynamic change curve of the SOE by combining current and voltage data acquired in real time;
And S39, outputting the corrected SOE dynamic change curve to a main control unit for guiding charge and discharge management of the battery pack.
And S4, analyzing the prediction result by combining a long-term performance decay mechanism, evaluating the residual life of the battery pack, and outputting the evaluation result.
In the step S4, the battery life evaluation method includes the steps of:
S41, acquiring capacity attenuation data of the battery pack under different cycle times, wherein the capacity attenuation data comprise a capacity retention rate and an internal resistance change rate;
s42, firstly, collecting capacity attenuation data of the battery pack under different cycle times, including a capacity retention rate and an internal resistance change rate, analyzing a performance attenuation mechanism of the battery pack in long-term operation, and identifying key influence factors including electrochemical side reaction, thermal runaway risk and mechanical fatigue;
S43, defining life assessment indexes based on a performance decay mechanism, wherein the life assessment indexes comprise cycle life, calendar life and safety life;
S44, training the acquired attenuation data by using a machine learning algorithm to generate a life assessment model;
s45, embedding a life evaluation model into the life evaluation module for predicting the residual life of the battery pack.
The machine learning algorithm is a support vector machine or neural network algorithm and is used for carrying out nonlinear fitting and prediction on capacity fading data.
The monitoring device and the main control unit are in data transmission through a special communication link, the communication coverage area of the special communication link is an area taking the center point of the battery pack as the center and taking half of the maximum geometric dimension of the battery pack as the radius, the SOE dynamic prediction module receives the output result of the P2D model and generates the SOE dynamic prediction result of the battery pack according to the result, and the life assessment module is combined with a long-term performance attenuation mechanism to assess the residual life of the battery pack and output the assessment result.
When the P2D model is constructed, the interior of the battery is first divided into a plurality of microcells, each microcell comprising a positive electrode region, a negative electrode region and a separator region. Ion conduction is realized between the positive electrode region and the negative electrode region through the diaphragm region, and the diaphragm region simultaneously plays a role in preventing electrons from directly flowing.
And constructing an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation, defining electrochemical reaction rate, heat conduction coefficient and mechanical stress distribution for each microcell, and respectively describing electrochemical reaction process, heat transfer behavior and mechanical deformation condition inside the battery.
The electrochemical reaction process is modeled by an electrochemical field, and comprises a lithium ion solid-phase diffusion equation, a liquid-phase transmission equation and a Butler-Volmer equation.
First, a conservation equation of the internal electrode (positive/negative electrode) and the electrolyte of the lithium ion battery is established based on the Newman frame, including a lithium ion solid phase diffusion equation:
,
wherein the boundary conditions include particle center symmetry:
,
surface reaction flux:
,
Wherein c s is solid-phase lithium concentration, D s is solid-phase diffusion coefficient, j is electrode reaction current density, F is faraday constant, and r is radial coordinate in a spherical coordinate system.
The liquid phase transmission equation comprises a conservation equation and a charge conservation equation of the lithium ion concentration in the electrolyte and the potential coupling, wherein the conservation equation of the lithium ion concentration in the electrolyte and the potential coupling is as follows:
,
the conservation of charge equation is:
,
wherein, v is a vector differential operator, c e is the liquid-phase lithium concentration, ε e is the volume fraction of the electrolyte, For the effective liquid phase diffusion coefficient,Is the migration number of lithium ions and is used for preparing the lithium ion battery, Is of liquid phase potential, K eff ,The effective conductivity of liquid-phase lithium and the diffusion conductivity of liquid-phase lithium are obtained;
the Butler-Volmer equation first describes the electrode/electrolyte interface reaction kinetics:
,
Exchange current density j 0 expression:
,
The overpotential η is defined as:
,
Where a a 、αc is the anode and cathode transfer coefficient, U ocp is the open circuit potential, Is the surface concentration of the solid-phase lithium,Is a solid phase potential. R is a gas constant, T is absolute temperature, and k is an intrinsic reaction rate constant;
the heat transfer behavior is represented by thermodynamic field coupling and comprises an energy conservation equation and temperature sensitive parameter correction, wherein the total heat production rate Q of the energy conservation equation comprises:
Joule heat: ,
Reaction heat: ,
Polarized heat: ,
The heat conduction equation:
,
Wherein, the In order to achieve a material density of the material,In order to fix the specific heat capacity under pressure,Represents the volumetric heat capacity, lambda is the thermal conductivity,Is the effective conductivity of solid-phase lithium;
temperature sensitive parameter correction is represented by the temperature dependence of diffusion coefficient and conductivity:
,
Wherein D represents the diffusion coefficient, D (T) represents the diffusion coefficient at temperature T, Expressed at a reference temperatureThe diffusion coefficient at the bottom, E a, is the activation energy, and T ref is the reference temperature.
Mechanical equilibrium equation based on the combination of solid phase diffusion equation and elastic mechanical equation to calculate the internal stress of the particle:
,
Wherein E is elastic modulus, c s is solid-phase lithium concentration, c 0 is solid-phase lithium initial concentration,Indicating the maximum intercalation concentration of solid-phase lithium,Is the relative amount of deformation of a material under a force or environmental change.
In S32, the electrochemical reaction rate, the thermal conductivity and the mechanical stress distribution of each region are defined as follows:
The electrochemical reaction rate adopts a Butler-Volmer equation to describe the reaction kinetics of an electrode/electrolyte interface;
The heat conduction coefficient is determined by a positive electrode region and a negative electrode region together with a diaphragm region, the positive electrode region consists of an active substance, a conductive agent, a binder and a pore electrolyte, and the equivalent thermal conductivity w eff is calculated by a mixed model:
,
Wherein mu i is the volume fraction of each component, and w i is the intrinsic thermal conductivity of the material (e.g. about 150W/(m.K) for graphite negative electrode, about 1.5W/(m.K) for lithium iron phosphate positive electrode).
The separator region is dominated by the material properties, the separator is typically a microporous Polyethylene (PE) or polypropylene (PP) film, the thermal conductivity is low (about 0.3-0.5W/(mK)), and the closed cell effect (pore closure at high temperature leading to increased thermal resistance) is considered.
The mechanical stress distribution is also determined by the positive and negative electrode regions and the separator region. The stress source of the anode and cathode regions is that lithium ions are intercalated/deintercalated to cause volume change of active particles (such as about 10% of graphite expansion rate and 300% of silicon-based material) to induce local stress. The sources of stress for the diaphragm region are external pressure conduction and thermo-mechanical stress. External pressure conduction-battery packaging pressure is transmitted to the diaphragm through the pole piece, and the compression resistance of the battery packaging pressure is considered. Thermal mechanical stress-temperature changes result in the difference in thermal expansion coefficients of the separator and the electrode (e.g., 23X 10- 6/K for aluminum current collector, about 200X 10- 6/K for PE separator), creating interfacial shear stress.
Based on the collected operation parameters, discretizing an electrochemical reaction equation, a thermal conduction equation and a mechanical balance equation by adopting a finite difference method to form a P2D model of multi-physical field coupling inside the battery. Wherein, the solid phase diffusion equation adopts an implicit Euler method discrete time term or a central difference method discrete space term. The nonlinear term of the Butler-Volmer equation is linearized by newton's iteration.
After the model is initialized through the computing platform, the multi-physical field coupling relation inside the battery can be accurately described. Through the process, the P2D model realizes high-precision description of the internal state of the battery, and lays a foundation for subsequent SOE dynamic prediction and life assessment.
In SOE dynamic prediction, initial state parameters of the battery pack are first defined, including SOC (State of Charge), SOH (State of Health) and the current ambient temperature. And calculating the energy change rate of the battery pack under different working conditions based on the established P2D model, and deducing a dynamic change curve of the SOE. In order to improve the prediction accuracy, a dynamic response algorithm is introduced, and the SOE dynamic change curve is corrected by combining current and voltage data acquired in real time. The corrected SOE dynamic change curve is output to a monitoring system for guiding charge and discharge management of the battery pack. In the process, the P2D model ensures the accuracy of SOE prediction results through accurate description of electrochemical reaction rate, thermal conductivity and mechanical stress distribution. For example, during acceleration of an electric bus, the current demand of the battery pack suddenly increases, and the P2D model can quickly respond and adjust the SOE dynamic change curve, so that performance attenuation caused by overcharge or overdischarge is avoided.
In the life evaluation process, capacity attenuation data of the battery pack at different cycle times, including a capacity retention rate and an internal resistance change rate, are collected first. By analyzing the performance decay mechanism in long-term operation, key factors affecting the service life of the battery, such as electrochemical side reactions, thermal runaway risks and mechanical fatigue, are identified.
1. Performance decay driven by electrochemical side reactions
1. Structural collapse of positive electrode material
The positive electrode material (such as ternary lithium and lithium iron phosphate) repeatedly undergoes lithium ion deintercalation in the charge and discharge process, so that the volume expansion and contraction are caused. Long-term stress accumulation can collapse the material lattice, block lithium ion intercalation channels and attenuate capacity. For example, lithium cobalt oxide may decompose to cobalt oxide and release oxygen at high temperatures, further exacerbating structural failure.
2. Negative electrode lithium precipitation and SEI film thickening
Under the low temperature or overcharge condition, the speed of lithium ions inserting into the negative electrode is delayed, so that metal lithium is precipitated on the surface of the negative electrode. The lithium separation not only consumes active lithium, but also punctures the diaphragm to cause internal short circuit. Meanwhile, the SEI film (solid electrolyte interface) repeatedly breaks and repairs in cycles, increases in thickness, hinders lithium ion transport, and causes an increase in internal resistance.
3. Electrolyte decomposition and conductive salt degradation
Organic solvents (such as carbonates) in the electrolyte are subjected to oxidative decomposition at high temperature or high voltage to generate combustible gases such as hydrogen, ethylene and the like, and unstable intermediate products are generated. And after degradation of conductive salts such as LiPF6, HF acid is generated, electrode materials are corroded, and capacity fading is accelerated. Accumulation of decomposition products also plugs the membrane pores, reducing ion mobility.
2. Risk of thermal runaway and chain reaction
1. Thermal runaway trigger mechanism
Internal shorting (e.g., lithium evolution penetrating the membrane), overcharge and overdischarge, or mechanical damage can cause local heat generation rates to exceed the heat dissipation capacity, triggering the following chain reactions:
and at the temperature of between 90 and 150 ℃, decomposing the SEI film, and reacting the electrolyte with the cathode to release heat.
And at the stage of 3.150-250 ℃, the separator melts to cause larger-scale short circuit, and the positive electrode material is decomposed and releases oxygen.
And 4. The stage of 250 ℃ is that the electrolyte burns vigorously, the battery shell breaks to release combustible gas, and explosion is initiated.
5. Thermal diffusion and module level runaway
The heat generated by thermal runaway of an individual cell is transferred to an adjacent cell by thermal conduction, convection and radiation, creating a "domino effect" of thermal runaway propagation. For example, lithium iron phosphate batteries can have thermal runaway propagation speeds of 5-10cm/min.
3. Mechanical fatigue and material degradation
Current collector corrosion and mechanical failure
The copper current collector is oxidized and dissolved during overdischarge (voltage > 1.5V), deposited copper metal damages the negative electrode structure, and the aluminum current collector is corroded by HF in the electrolyte, so that conductivity is reduced. Interfacial exfoliation of the current collector and active material also increases internal resistance during long-term cycling.
Electrode material particle microcrack
In the charge and discharge process, the electrode material repeatedly expands and contracts (for example, the volume change of the silicon-based negative electrode reaches 300%), so that particles are broken, the contact resistance is increased, and the capacity is rapidly attenuated.
Structural support fatigue
The fixed support and the connecting piece of the battery module are easy to generate stress fatigue in vibration and temperature circulation, and can lead to displacement and connection looseness of the battery cell, thereby causing local overheating or short circuit.
Based on these performance decay mechanisms, life assessment indicators such as cycle life, calendar life, and safety life are defined. Training the acquired attenuation data by using a machine learning algorithm to generate a life assessment model.
The process is generally implemented by software algorithm using the prior art, and the general process comprises
1. Data acquisition and cleaning
2. Feature extraction
3. Data annotation
4. Model selection and training
5. Model verification and evaluation
6. Model deployment and online update
The model is embedded in a monitoring system for predicting the remaining life of the battery pack. In the process, the life assessment model is matched with the P2D model, the life assessment model depends on the multi-physical field coupling information provided by the P2D model, and the P2D model optimizes the prediction capacity through feedback of the P2D model. For example, after the electric bus runs for a long time, the battery pack may have capacity fading phenomenon, and the life evaluation module can predict the remaining life according to the historical data and provide basis for subsequent maintenance.
In the actual running process, when the vehicle is in high-load working conditions such as acceleration or climbing, the monitoring equipment collects voltage, current and temperature data of the lithium iron phosphate battery pack in real time and transmits the voltage, current and temperature data to the main control unit through a special communication link. The main control unit utilizes the P2D model to dynamically predict the state of the battery pack, and combines the SOE dynamic prediction module to generate the current SOE value. Meanwhile, the life assessment module assesses the remaining life of the battery pack according to a long-term performance decay mechanism and feeds back the result to the vehicle control system. The driver can know the battery state in real time through the vehicle-mounted display screen, and take corresponding measures according to the prompt, such as adjusting the driving mode or planning the charging time. The real-time monitoring and evaluating mechanism not only improves the running efficiency of the battery pack, but also prolongs the service life of the battery pack.
In an industrial energy storage scene, the system has wide application value as well. For example, in a photovoltaic energy storage system, a lithium iron phosphate battery pack needs to be charged and discharged frequently as an energy storage device. The monitoring device collects the operation data of the battery pack in real time and transmits the operation data to the main control unit through a special communication link. The main control unit utilizes the P2D model to dynamically predict the state of the battery pack, and the service life of the battery pack is estimated by combining with the service life estimation module. Through accurate SOE dynamic prediction and life assessment, the system can optimize the charge and discharge strategy of the battery pack, and avoid the phenomenon of overcharge or overdischarge, thereby effectively prolonging the service life of the battery pack and improving the operation efficiency of the system.
In conclusion, the system realizes accurate modeling of the complex multi-physical field interaction process inside the battery by introducing the P2D model of electrochemical-thermal-mechanical multi-physical field coupling. And data transmission is carried out between the monitoring equipment and the battery pack main control unit through a special communication link, so that the real-time performance and accuracy of data acquisition are ensured. The P2D model is combined with the historical operation data and the real-time state parameters to generate an SOE dynamic prediction result, and the simple and efficient structural design ensures that the model converges more quickly in the training process and the prediction precision is higher. Through deep analysis of a long-term performance attenuation mechanism of the battery pack, more accurate service life assessment is realized, and the high-efficiency and reliable battery management requirements under complex working conditions are met.
Claims (8)
1. The method for dynamically predicting SOE and evaluating the service life of the lithium iron phosphate battery pack by fusing the P2D model is characterized by comprising the following steps of:
s1, basic operation data of a lithium iron phosphate battery pack are collected through a sensor network arranged in the battery pack, and a special communication link between the battery pack and monitoring equipment is established;
S2, uploading historical operation data, real-time state parameters and environment variables of the battery pack through a special communication link;
s3, generating SOE dynamic prediction results of the battery pack by using an electrochemical-thermal-mechanical multi-physical field coupled P2D model based on historical operation data and real-time state parameters;
s4, analyzing the prediction result by combining a long-term performance attenuation mechanism, evaluating the residual life of the battery pack, and outputting an evaluation result;
In the step S3, the SOE dynamic prediction result is calculated by using an electrochemical-thermal-mechanical multi-physical field coupled P2D model, and the specific steps are as follows:
s31, collecting operation parameters of the lithium iron phosphate battery pack, including voltage, current, temperature, internal resistance and electrolyte concentration distribution;
s32, dividing the interior of the battery into a plurality of microcells, wherein each microcell comprises an anode region, a cathode region and a diaphragm region, constructing an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation, defining the electrochemical reaction rate, the heat conduction coefficient and the mechanical stress distribution of each region, and respectively describing the electrochemical reaction process, the heat transfer behavior and the mechanical deformation condition in the interior of the battery;
S33, performing discretization processing on an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation in the battery by using a finite difference method based on the collected operation parameter data;
s34, forming a P2D model of multi-physical field coupling inside the battery through coupling of electrochemical reaction rate, thermal conductivity and mechanical stress distribution;
s35, inputting the discretized P2D model with multiple physical field couplings into a computing platform of a main control unit, and completing initialization of the P2D model;
S36, defining initial state parameters of the battery pack, including SOC, SOH and current environment temperature;
S37, calculating the energy change rate of the battery pack under different working conditions based on the P2D model, and deducing a dynamic change curve of the SOE through the energy change rate;
s38, introducing a dynamic response algorithm, and correcting a dynamic change curve of the SOE by combining current and voltage data acquired in real time;
and S39, outputting the corrected SOE dynamic change curve to a main control unit for guiding charge and discharge management of the battery pack.
2. The method for dynamic SOE prediction and life assessment of a lithium iron phosphate battery fused with a P2D model according to claim 1, wherein in step S1, the method for establishing the dedicated communication link is as follows:
S11, acquiring an identity identifier of monitoring equipment accessed into a battery pack;
S12, taking a central point of the battery pack as a circle center, and taking half of the maximum geometric dimension of the battery pack as a radius to define a monitoring area for communication coverage;
S13, judging whether a relay node exists in the monitoring area, if not, directly communicating the monitoring equipment with a battery pack main control unit to complete the establishment of a special communication link, and if so, entering step S14;
And S14, defining a subarea which comprises a relay node and is concentric with the monitoring area as a relay coverage area by taking the central point of the battery pack as a circle center, and completing establishment of a special communication link by the monitoring equipment through communication between the relay node and the battery pack main control unit.
3. The method for dynamic prediction and life assessment of SOE of lithium iron phosphate battery fused with P2D model according to claim 1, wherein in step S4, the method for life assessment of battery comprises the steps of:
S41, acquiring capacity attenuation data of the battery pack under different cycle times, wherein the capacity attenuation data comprise a capacity retention rate and an internal resistance change rate;
s42, firstly, collecting capacity attenuation data of the battery pack under different cycle times, including a capacity retention rate and an internal resistance change rate, analyzing a performance attenuation mechanism of the battery pack in long-term operation, and identifying key influence factors including electrochemical side reaction, thermal runaway risk and mechanical fatigue;
s43, defining life assessment indexes based on a performance decay mechanism, wherein the life assessment indexes comprise cycle life, calendar life and safety life;
S44, training the acquired attenuation data by using a machine learning algorithm to generate a life assessment model;
s45, embedding a life evaluation model into the life evaluation module for predicting the residual life of the battery pack.
4. The method for dynamically predicting SOE and evaluating life of a lithium iron phosphate battery fused with a P2D model according to claim 3, wherein the machine learning algorithm is a support vector machine or a neural network algorithm, and is used for performing nonlinear fitting and prediction on capacity fading data.
5. The method for dynamically predicting and evaluating the service life of the lithium iron phosphate battery pack by fusing the P2D model according to claim 2 is characterized in that data transmission is carried out between the monitoring equipment and the main control unit through a special communication link, the communication coverage area of the special communication link is an area taking the center point of the battery pack as the center and taking half of the maximum geometric dimension of the battery pack as the radius, the SOE dynamic prediction module receives the output result of the P2D model and generates the SOE dynamic prediction result of the battery pack according to the output result, and the service life evaluation module evaluates the residual service life of the battery pack by combining a long-term performance attenuation mechanism and outputs the evaluation result.
6. The method for dynamically predicting SOE and evaluating life of the lithium iron phosphate battery fused with the P2D model according to claim 1, wherein a numerical calculation mode of fixed step length is adopted when discretizing an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation by the finite difference method.
7. The method for dynamically predicting and evaluating the life of the SOE of the lithium iron phosphate battery fused with the P2D model according to claim 1, wherein in S32, an electrochemical reaction equation, a heat conduction equation and a mechanical balance equation are constructed:
the electrochemical reaction process is modeled by an electrochemical field, and comprises a lithium ion solid-phase diffusion equation, a liquid-phase transmission equation and a Butler-Volmer equation;
lithium ion solid phase diffusion equation:
wherein the boundary conditions include particle center symmetry:
surface reaction flux:
Wherein t represents time, c s is solid-phase lithium concentration, D s is solid-phase diffusion coefficient, j is electrode reaction current density, F is faraday constant, R is radial coordinate in a spherical coordinate system, and R s represents radius of solid-phase lithium particles;
The liquid phase transmission equation comprises a conservation equation and a charge conservation equation of the lithium ion concentration in the electrolyte and the potential coupling, wherein the conservation equation of the lithium ion concentration in the electrolyte and the potential coupling is as follows:
the conservation of charge equation is:
Wherein, the For vector differential operator, c e is liquid phase lithium concentration, ε e is electrolyte volume fraction,For the effective liquid phase diffusion coefficient,Is the migration number of lithium ions and is used for preparing the lithium ion battery,Is at the potential of the liquid phase, The effective conductivity of liquid-phase lithium and the diffusion conductivity of liquid-phase lithium are obtained;
The Butler-Volmer equation first describes the electrode/electrolyte interface reaction kinetics:
Exchange current density j 0 expression:
The overpotential η is defined as:
where a a、αc is the anode and cathode transfer coefficient, U ocp is the open circuit potential, Is the surface concentration of the solid-phase lithium,R is a gas constant, T is absolute temperature, and k is an intrinsic reaction rate constant;
The heat transfer behavior is represented by thermodynamic field coupling and comprises an energy conservation equation and temperature sensitive parameter correction, wherein the total heat production rate Q of the energy conservation equation comprises:
Joule heat:
heat of reaction, Q rxn =jη
Polarized heat:
The heat conduction equation:
Wherein ρ is the material density, C p is the constant pressure specific heat capacity, ρC p is the volumetric heat capacity, λ is the thermal conductivity, and δ eff is the solid-phase lithium effective electrical conductivity;
temperature sensitive parameter correction is represented by the temperature dependence of diffusion coefficient and conductivity:
Wherein D represents the diffusion coefficient, D (T) represents the diffusion coefficient at temperature T, Representing the diffusion coefficient at a reference temperature T ref, E a being the activation energy, T ref being the reference temperature;
And (3) calculating the internal stress xi of the particles based on the combination of the solid phase diffusion equation and the elastic mechanical equation by a mechanical equilibrium equation:
wherein E is the elastic modulus, c s is the solid-phase lithium concentration, c 0 is the initial concentration of solid-phase lithium, c s,max is the maximum intercalation concentration of solid-phase lithium, and ε is the relative deformation of the material under the stress or environmental change.
8. The method for dynamically predicting and estimating SOE of lithium iron phosphate battery fused with P2D model according to claim 1 or 7, wherein in S32, the electrochemical reaction rate, thermal conductivity and mechanical stress distribution of each region are defined as follows:
The electrochemical reaction rate adopts a Butler-Volmer equation to describe the reaction kinetics of an electrode/electrolyte interface;
The heat conduction coefficient is determined by a positive electrode region and a negative electrode region together with a diaphragm region, the positive electrode region consists of an active substance, a conductive agent, a binder and a pore electrolyte, and the equivalent thermal conductivity w eff is calculated by a mixed model:
weff＝Σμiwi
wherein μ i is the volume fraction of each component, w i is the intrinsic thermal conductivity of the material;
The diaphragm region is dominated by material properties;
the mechanical stress distribution is determined by the positive electrode region, the negative electrode region and the diaphragm region, wherein the stress sources of the positive electrode region and the negative electrode region are that the volume of active particles is changed due to lithium ion intercalation/deintercalation to induce local stress, the stress sources of the diaphragm region are external pressure conduction and thermomechanical stress, the external pressure conduction is that the battery packaging pressure is transmitted to the diaphragm through a pole piece and the anti-compression performance of the battery packaging pressure needs to be considered, and the thermomechanical stress is that the thermal expansion coefficient difference of the diaphragm and an electrode is caused by temperature change to generate interfacial shear stress.
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