Failure-Mechanism-Driven Inverse Design and Optimization Procedure for Battery Lifetime Extension

Precise design and optimization of lithium-ion batteries (LIBs) remain challenging due to their intricate, dynamic degradation mechanisms and the competitive interactions. Building upon a mechanism-driven LIB lifetime prediction model based on capacity degradation, we propose an inverse design and optimization procedure (IDOP) that integrates parameter sensitivity analysis (PSA) and multiobjective optimization (MOO). For modeling, we employ directly adjustable design parameters of the anode and electrolyte and indirectly derived interfacial characteristics of anode/electrolyte interface. The PSA results indicate that areal density, particle radius, and interface characteristics exert a significant influence on battery lifetime. Using data from a reference LiNi_0.6Co_0.1Mn_0.3O₂||graphite pouch cell for assessment, the MOO predicts a battery lifetime extension of up to 26.63% (70.97%) and 32.76% (138.41%) at 25 and 45 °C by optimizing direct (indirect) factors, respectively. Evaluation of the MOO-optimized factors using the failure-mechanism-driven model demonstrates remarkable alignment in capacity degradation trajectories. The IDOP framework is a promising approach to improve the design and optimization efficiency for developing better LIBs.