CN111241685A - Lithium battery pack system reliability optimization design method based on multi-physical-field simulation and response surface analysis method - Google Patents
Lithium battery pack system reliability optimization design method based on multi-physical-field simulation and response surface analysis method Download PDFInfo
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Abstract
The invention relates to a lithium battery pack system reliability optimization design method based on a multi-physical-field simulation and response surface analysis method. The method comprises the steps of formulating a redundancy design scheme according to the size and reliability requirements of a physical model of the lithium battery pack; developing a response surface experimental scheme design by determining a battery arrangement mode and design parameters to be optimized; carrying out simulation analysis on the physical process of the system by establishing a lithium battery pack multi-physical field model; evaluating and analyzing the system reliability by constructing a multi-physical field coupling-based lithium battery pack multi-state system reliability model and a randomness model; and constructing a response surface based on all the design schemes and the analysis results thereof, and completing the reliability optimization design work of the lithium battery pack system by using a response surface analysis method. The method integrates a multi-physical-field simulation technology, a system reliability method, a random uncertainty method and a response surface analysis method, can scientifically and accurately describe a physical process, and can efficiently obtain an optimal redundancy and layout design scheme.
Description
Technical Field
The invention relates to the field of system reliability optimization design, in particular toSimulation and response surface division based on multiple physical fields Analytical method lithium battery pack system reliability optimization design method。
Background
With the rapid development of lithium ion battery technology, lithium batteries are widely applied to power systems of electric vehicles. With the development and maturity of the electric automobile industry, the market demand for electric automobiles is increasing. Therefore, higher requirements are also placed on the service life and reliability of the lithium battery pack for vehicles.
There are many ways to improve the reliability of lithium batteries (LIBPs). Among them, the fault tolerant technique based on the redundancy design is an effective method. While improving battery system reliability, redundant designs are often accompanied by structural and layout changes that adversely affect battery pack reliability. Therefore, how to increase the redundant units, optimize the layout design, and find the optimal reliability design scheme from a large number of schemes is very important, and is also a technical difficulty of reliable optimization design of the LIBPs system.
For the reliability optimization design of the LIBPs system, firstly, modeling and analyzing the reliability of the LIBPs system, and developing the reliability optimization design work based on the analysis result. The existing LIBPs reliability modeling, analyzing and optimizing methods mainly include a reliability cartography (RBD), a Fault Tree Analysis (FTA), a Markov model (Markov), a reliability analysis method based on a Universal Generation Function (UGF), a Monte Carlo (Monte Carlo) simulation method, a multi-physics simulation method, and the like.
Among the conventional reliability methods, the RBD method is the earliest and the most basic method. The basic models are series connection, parallel connection, standby, voting and the like, and can better express the connection mode of the batteries. Therefore, most reliability analysis and optimization design of the LIBPs system are developed based on the RBD model. As a static modeling and analysis method, the RBD method has the advantage of simplicity and intuition, but it is difficult to describe complex systems such as timing, coupling, systems subject to environmental influences and human factors. The FTA method is a graphic method, is clear and easy to understand, and facilitates deep qualitative and quantitative analysis of complex logical relations among a plurality of events. The Markov model can accurately describe the dependency relationship among fault mechanisms and comprehensively reflect various dynamic behaviors, so that the reliability of the system is comprehensively analyzed and optimized, and the method is often used for analyzing the dynamic characteristics of the LIBPs system. The UGF-based reliability analysis method is mainly used for reliability analysis of polymorphic systems, can well analyze polymorphic characteristics in LIBPs system degradation, and is usually combined with an RBD method. The Monte Carlo simulation method is often combined with other reliability methods for analysis, and is not only used for simulating and optimizing the reliability of the LIBPs system, but also used for simulating the random uncertainty of the analysis system. The methods are based on statistical rules and logical relations, and cannot accurately reflect the complicated physical process and the coupled degradation process of the lithium battery pack. In recent years, with the development of multi-physics simulation technology, many scholars perform reliability analysis and optimization on a battery pack based on a multi-physics simulation method. According to literature reports, the physical models of the LIBPs system mainly include electrochemical models, thermal models and fluid mechanics models. However, few have combined these three models simultaneously to analyze and optimize the reliability of the LIBPs system. Furthermore, the complex association of thousands of cells is difficult to handle by physical analysis alone, and should be analyzed in combination with logical methods.
In summary, the traditional system reliability theory and method are widely applied to reliability analysis and optimization design of the LIBPs system, but the reliability analysis developed by the method only stays at the system logic level and cannot consider the actual physical model. The multi-physics simulation method can better describe the physics representation of the LIBPs system, but lacks the description capability of random uncertainty in actual engineering. Therefore, in the aspect of reliability modeling analysis of the lithium battery pack, a complementary relationship exists between the multi-physical-field simulation method and a traditional system reliability theoretical method, and the reliability analysis and optimization work of the LIBPs system can be scientifically and accurately carried out by organically combining the multi-physical-field simulation method and the traditional system reliability theoretical method.
On the other hand, the multi-physics simulation method based on the fault physics comprehensively considers the influence of factors such as current, temperature, vibration and the like, and can scientifically and accurately describe the degradation and failure of the LIBPs. However, the establishment of multi-physics coupled models such as electrochemical, thermal, and hydrodynamic models is rather complicated and difficult to solve for analytical processes. The modeling and calculation costs of building a high precision physical model from the system as a whole are unacceptable. Therefore, the exhaustive reliability optimization design work is carried out by only depending on the multi-physical simulation method and the traditional system reliability method, so that not only is a large amount of calculation time and cost required, but also an optimal design scheme can not be found even.
In view of this, a method for optimally designing the reliability of a lithium battery pack system based on multi-physical-field simulation and response surface analysis is needed.
Disclosure of Invention
The invention aims to solve the problems in the prior art of the LIBPs system reliability optimization design, and provides a LIBPs system reliability optimization design method based on multi-physical-field simulation and response surface analysis. According to the method, a multi-physical-field simulation technology, a system reliability method and a response surface analysis method are coupled for the first time according to the structure and the characteristics of the LIBPs system, and a reliability optimization design method and a reliability optimization design flow of the LIBPs system are established. The method realizes the modeling, analysis and optimization design of the LIBPs system reliability based on the physics of failure for the first time, not only can accurately describe the physical process of the LIBPs system degradation/failure, but also can efficiently complete the reliability optimization design work, and is beneficial to improving the accuracy of the LIBPs system reliability evaluation and the efficiency of the optimization design work.
The invention provides a LIBPs system reliability optimization design method based on multi-physical-field simulation and response surface analysis, which mainly comprises the following steps:
step 1: the LIBPs system size and reliability design requirements are determined. Determining the size and reliability requirements of a physical model of the lithium battery pack according to the actual application condition of the LIBPs system;
step 2: several redundancy designs are formulated. The redundancy design scheme is established by considering not only the reliability design requirement of the LIBPs system but also the physical size of the LIBPs system, so the number of the redundancy design scheme is limited. In addition, cost, power, capacity and other factors may need to be considered in actual engineering;
and step 3: and selecting a redundancy design scheme of the LIBPs system, and further determining a layout mode of a battery in the LIBPs system and parameters needing to be optimized. Determining parameters to be optimized according to the arrangement layout mode of the battery monomers in the battery pack, wherein the parameters are mainly parameters of the space between the battery monomers capable of determining the layout of the battery pack, and can be the distance in multiple directions, and can also be parameters such as the proportional coefficient and the angle of the space;
and 4, step 4: designing and constructing an experimental scheme of a LIBPs system reliability response surface; the design methods of the experimental scheme include a Central composite design method, a Box-Behnken design method, a mixed design method (Mixturedesign) and the like;
and 5: and establishing a LIBPs system multi-physical-field coupling model, completing model verification and carrying out multi-physical-field coupling simulation analysis. The LIBPs system multi-physical field model comprises an electrochemical model (comprising electrochemical reaction and side reaction), a thermal model, a fluid dynamic model and the like;
step 6: and (5) carrying out physical characterization analysis such as temperature field, degradation, failure and the like based on the multi-physical-field coupling simulation result in the step 5, and calculating to obtain corresponding physical quantities. And (3) obtaining a temperature field distribution result of the LIBPs system based on simulation analysis of an electrochemical-thermal-fluid dynamic coupling model. The physical quantities of battery degradation and failure are obtained by combining the simulation calculation of a battery side reaction model based on the temperature field analysis result;
and 7: and establishing a LIBPs system reliability model based on an RBD method, and establishing a degradation randomness model by analyzing the characteristics of battery degradation randomness. The distribution of the randomness model mainly obeys normal distribution, Weibull distribution and the like;
and 8: based on the physical quantities of the temperature field, the degradation, the failure and the like obtained by analysis and calculation in the step 6, carrying out system reliability analysis and evaluation based on a reliability analysis method of UGF to obtain reliability indexes (reliability and the like);
and step 9: repeating the step 5 to the step 8 until all experimental schemes in the step 4 are completed, constructing a response surface of the LIBPs system reliability according to the reliability evaluation result and the layout design size parameters, and fitting by adopting a least square nonlinear regression method;
step 10: verifying whether the constructed response surface model meets the accuracy requirement, if not, returning to the step 4 to modify or redesign the experimental scheme, increasing or deleting experimental data points according to the actual fitting condition, and if the fitting effect is poor, replacing the response surface experimental design method for redesigning;
step 11: optimizing a LIBPs system reliability response surface model meeting the accuracy requirement by adopting a Genetic Algorithm (GA), finding out a design parameter with optimized reliability, and determining the optimized layout of the battery pack under the redundancy design scheme;
step 12: repeating the step 3 to the step 11 until the optimizing work of all the LIBPs system redundancy design schemes is completed;
step 13: and determining the optimal redundancy design scheme and the optimal layout of the LIBPs system by comparing the redundancy design schemes and the optimal layout thereof.
The invention has the following excellent effects: in the field of LIBPs system reliability optimization design, a multi-physical-field simulation technology, a system reliability theoretical method, a random uncertainty theoretical method and a response surface analysis method are integrated comprehensively. By organically combining the methods, the advantages of the multi-physical-field simulation technology and the response surface analysis method are simultaneously exerted. The physical representation of the operation and the degradation of the battery pack can be scientifically and accurately described by using a multi-physical simulation method, and the problem of combined explosion caused by the change of a plurality of design variables can be avoided by using a response surface analysis method. Therefore, the method can effectively obtain the optimal redundancy and layout design scheme of the battery pack meeting the high reliability requirement, and greatly reduces the calculation amount.
Drawings
FIG. 1 is a flow chart of a lithium battery pack system reliability optimization design method based on a multi-physics field simulation and response surface analysis method
FIG. 2 is a schematic diagram of an equidistant cross arrangement of the LIBPs system
FIG. 3 shows the optimized layout structure and temperature distribution of LIBPs system with different redundancy design schemes
FIG. 4 is a schematic diagram of a LIBPs multi-state system reliability model based on multi-physical field coupling
Detailed Description
In order to make the features and advantages of the present invention more apparent, the following description is given by way of example and in detail with reference to the accompanying drawings. The specific implementation steps of the user for optimizing the reliability of the LIBPs system based on the method are as follows:
step 1: the LIBPs system size and reliability design requirements are determined. Taking a 3-and-5-string battery pack of size 180 × 108 × 65mm (x × y × z) as an example, reliability is required to be maintained above 0.95 after 2000 cycles;
step 2: considering the reliability design requirement and the physical size of the LIBPs system, a redundancy design scheme is made, and taking 3 parallel 5-string, 3 parallel 8-string and 4 parallel 6-string lithium battery packs as examples;
and step 3: selecting 3 parallel 8 strings of LIBPs systems, determining that the battery layout mode of the LIBPs systems is in equal-interval cross arrangement, and setting the design parameter to be optimized as x1、x2、x3FIG. 2 shows a schematic diagram of an LIBPs system with equal-pitch cross arrangement;
and 4, step 4: the range of the design parameters is shown in table 1 according to the geometrical size limitation and symmetry. Wherein x3The following conditions are satisfied (n is the number of parallel branches):
x3+(n-1)·x2+18≤108 (1)
an experimental scheme for designing and constructing the LIBPs system reliability response surface is designed by using a Box-Behnken design method, as shown in Table 2, wherein values of-1, 0 and 1 respectively represent the lower limit, the middle value and the upper limit of a design parameter value.
TABLE 1 value ranges of different lithium battery pack design parameters
| Type (B) | x1/mm | x2/mm | x3/mm |
| 3 and 5 strings | [18,40.5] | [18,45] | [0,90-2*x2] |
| 3 and 8 strings | [18,23.14] | [18,45] | [0,90-2*x2] |
| 4 and 6 strings | [18,32.39] | [18,30] | [0,90-3*x2] |
And 5: establishing an electrochemical-thermal-hydrodynamic multi-physical-field coupling model of the LIBPs system, completing corresponding model verification, carrying out multi-physical-field coupling simulation analysis, and analyzing a physical process in the operation process of the LIBPs system. Wherein the electrochemical model adopts a quasi-two-dimensional model (P2D); the control equation of the thermal model is an energy conservation equation, and the boundary condition is determined according to a Newton cooling law and a Stefan-Boltzmann law; the fluid dynamic model consists of a Navisstokes (N-S) equation and a k-epsilon turbulence model; the mathematical equations of the model are described in detail.
Step 6: and (5) carrying out physical characterization analysis such as temperature field, degradation, failure and the like based on the multi-physical-field coupling simulation result in the step 5, and calculating to obtain corresponding physical quantities. Based on the simulation analysis of the electrochemical-thermal-fluid dynamic coupling model, the temperature field distribution result of the LIBPs system is obtained, as shown in FIG. 3. The physical quantities of battery degradation and failure are obtained by combining the simulation calculation of a battery side reaction model based on the temperature field analysis result;
and 7: based on the RBD method, a multi-physical-field coupled LIBPs multi-state system reliability model is established, as shown in FIG. 4. And a degradation randomness model is established by analyzing the characteristics of the degradation randomness of the battery. The normal distribution, Weibull distribution models are listed below;
normal distribution model:
weibull distribution model:
βfade(T)＝0.0003683·T2+0.02716·T-37.83 (4)
wherein T is temperature, N is number of cyclic charge and discharge, Cfade,NThe capacity degradation after N charge-discharge cycles.
And 8: based on the physical quantities of the temperature field, the degradation, the failure and the like obtained by analysis and calculation in the step 6, system reliability analysis and evaluation are carried out based on a reliability analysis method of UGF, and reliability indexes (reliability and the like) are obtained as shown in table 2; the UGF method and the U function expression thereof are as follows:
cell U function:
battery pack U function:
TABLE 2 experimental design scheme and results for LIBPs system based on Box-Behnken design method
And step 9: and (5) repeating the step (5) to the step (8) until all experimental schemes in the step (4) are completed, constructing a response surface of the LIBPs system reliability according to the reliability evaluation result and the layout design size parameters, and fitting by adopting a least square nonlinear regression method. The three-parameter response surface model is as follows:
the fitting coefficient results are shown in table 3.
TABLE 3 response surface model coefficients
Step 10: and randomly selecting design parameters, and comparing the system reliability analysis result based on multi-physical field coupling with the response surface model result to complete the accuracy verification of the response surface model. And through verification, the constructed response surface model meets the accuracy requirement. If not, returning to the step 4 to modify or redesign the experimental scheme, increasing or deleting experimental data points according to the actual fitting situation, and if the fitting effect is poor, replacing the response surface experimental design method for redesigning;
step 11: optimizing the LIBPs system reliability response surface model meeting the accuracy requirement by adopting a Genetic Algorithm (GA), finding out the design parameters of reliability optimization, and determining the optimal layout of the battery pack under the redundancy design scheme, as shown in Table 4;
step 12: repeating the steps 3 to 11 until the layout optimization work of all the LIBPs system redundancy design schemes is completed, wherein the result is shown in table 4, and the structural schematic diagram is shown in fig. 3;
TABLE 4 optimal design parameters (dimensionless design parameters in brackets)
| Parameter(s) | 3 and 5 strings | 3 and 8 strings | 4 and 6 strings |
| x1/mm | 40.50(1) | 23.14(1) | 32.40(1) |
| x2/mm | 27.45(-0.2381) | 28.80(-0.1429) | 25.80(0.3333) |
| x3/mm | 26.74(0.5238) | 21.60(0.3333) | 4.20(-0.3333) |
Step 13: by pairsComparing each redundancy design scheme and the optimized layout thereof, determining the LIBPs system optimal redundancy design scheme to be 4 parallel-6 strings in the redundancy design schemes, and determining the design parameters (x) of the optimized layout thereof1、x2、x3) 32.40, 25.80 and 4.20 mm.
The method provided by the invention realizes the reliability optimization design of the lithium battery pack system based on the multi-physical-field simulation and response surface analysis method, and the method not only can scientifically and accurately describe the physical process of the operation of the battery pack, but also greatly reduces the calculated amount of the simulation analysis of the system reliability and improves the efficiency of the optimization design work.
The above description is a preferred embodiment of the present invention, and it will be understood by those skilled in the art that the present invention may be modified and equivalents may be substituted without departing from the scope of the present invention.
Claims (1)
1. A lithium battery pack (LIBPs) system reliability optimization design method based on a multi-physical field simulation and response surface analysis method is characterized by comprising the following steps: it comprises the following steps:
step 1: the LIBPs system size and reliability design requirements are determined. Determining the size and reliability requirements of a physical model of the lithium battery pack according to the actual application condition of the LIBPs system;
step 2: several redundancy designs are formulated. The redundancy design scheme is established by considering not only the reliability design requirement of the LIBPs system but also the physical size of the LIBPs system, so the number of the redundancy design scheme is limited. In addition, cost, power, capacity and other factors may need to be considered in actual engineering;
and step 3: and selecting a redundancy design scheme of the LIBPs system, and further determining a layout mode of a battery in the LIBPs system and parameters needing to be optimized. Determining parameters to be optimized according to the arrangement layout mode of the battery monomers in the battery pack, wherein the parameters are mainly parameters of the space between the battery monomers capable of determining the layout of the battery pack, and can be the distance in multiple directions, and can also be parameters such as the proportional coefficient and the angle of the space;
and 4, step 4: designing and constructing an experimental scheme of a LIBPs system reliability response surface; the design methods of the experimental scheme include a Central composite design method, a Box-Behnken design method, a mixed design method (Mixturedesign) and the like;
and 5: establishing a LIBPs system multi-physical field coupling model, and carrying out multi-physical field coupling simulation analysis. The LIBPs system multi-physical field model comprises an electrochemical model (comprising electrochemical reaction and side reaction), a thermal model, a fluid dynamic model and the like;
step 6: and (5) carrying out physical characterization analysis such as temperature field, degradation, failure and the like based on the multi-physical-field coupling simulation result in the step 5, and calculating to obtain corresponding physical quantities. And (3) obtaining a temperature field distribution result of the LIBPs system based on simulation analysis of an electrochemical-thermal-fluid dynamic coupling model. The physical quantities of battery degradation and failure are obtained by combining the simulation calculation of a battery side reaction model based on the temperature field analysis result;
and 7: and establishing a LIBPs system reliability model based on an RBD method, and establishing a degradation randomness model by analyzing the characteristics of battery degradation randomness. The distribution of the randomness model mainly obeys normal distribution, Weibull distribution and the like;
and 8: based on the physical quantities of the temperature field, the degradation, the failure and the like obtained by analysis and calculation in the step 6, carrying out system reliability analysis and evaluation based on a reliability analysis method of UGF to obtain reliability indexes (reliability and the like);
and step 9: repeating the step 5 to the step 8 until all experimental schemes in the step 4 are completed, constructing a response surface of the LIBPs system reliability according to the reliability evaluation result and the layout design size parameters, and fitting by adopting a least square nonlinear regression method;
step 10: verifying whether the constructed response surface model meets the accuracy requirement, if not, returning to the step 4 to modify or redesign the experimental scheme, increasing or deleting experimental data points according to the actual fitting condition, and if the fitting effect is poor, replacing the response surface experimental design method for redesigning;
step 11: optimizing a LIBPs system reliability response surface model meeting the accuracy requirement by adopting a Genetic Algorithm (GA), finding out a design parameter with optimized reliability, and determining the optimized layout of the battery pack under the redundancy design scheme;
step 12: repeating the step 3 to the step 11 until the optimizing work of all the LIBPs system redundancy design schemes is completed;
step 13: and determining the optimal redundancy design scheme and the optimal layout of the LIBPs system by comparing the redundancy design schemes and the optimal layout thereof.
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