Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

Abstract

A 1D electrochemical-thermal model of an electrode pair of a lithium ion battery is developed in Comsol Multiphysics. The mathematical model is validated against the literature data for a 10 Ah lithium phosphate (LFP) pouch cell operating under 1 C to 5 C electrical load at 25 • C ambient temperature. The validated model is used to conduct statistical analysis of the most influential parameters that dictate cell performance; i.e., particle radius (r p ); electrode thickness (L pos ); volume fraction of the active material (ε s,pos ) and C-rate; and their interaction on the two main responses; namely; specific energy and specific power. To achieve an optimised window for energy and power within the defined range of design variables; the range of variation of the variables is determined based on literature data and includes: r p : 30-100 nm; L pos : 20-100 µm; ε s,pos : 0.3-0.7; C-rate: 1-5. By investigating the main effect and the interaction effect of the design variables on energy and power; it is observed that the optimum energy can be achieved when (r p < 40 nm); (75 µm < L pos < 100 µm); (0.4 < ε s,pos < 0.6) and while the C-rate is below 4C. Conversely; the optimum power is achieved for a thin electrode (L pos < 30 µm); with high porosity and high C-rate (5 C).

A systematic approach for electrochemical-thermal modelling of a large format lithium-ion battery for electric vehicle application

“…The derivation of the electrochemical model is discussed in detail within [35] for a 10 Ah LFP pouch cell and will therefore not be repeated here. For completeness, the governing equations and the boundary conditions that represent the P2D model are listed in Table 1.…”

“…As discussed within [35,62], out of the large number of electrochemical parameters electrode thickness (L i ), particle sizes (r p ), porosity (ε i ), diffusion coefficient (D i ), lithium concentration in the active materials (C s max , ) and reaction rate (k ) i in both electrodes are found to be the most crucial ones for modelling. It should be noted that i indicates the domain.…”

Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes

“…Recently, parametric studies and sensitivity analyses of the process parameters, using artificial neural networks (ANNs) combined with physico-chemical models, have been carried out [18,19]. ANNs are brain-inspired systems, which are one of the main machine learning tools.…”

The Effect of Active Material, Conductive Additives, and Binder in a Cathode Composite Electrode on Battery Performance

“…In addition, parameter sensitivity studies have also been performed to determine important design parameters and ultimately improve battery performance [6][7][8][9][10][11]. Zhang et al [7] performed sensitivity analyses of 30 different parameters using multi-physics modeling.…”