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

Area of Science:

• Materials Science
• Electrochemistry
• Battery Technology

Background:

• Lithium-ion battery (LIB) design and optimization face challenges due to complex degradation mechanisms and competing interactions.
• Existing models often struggle to capture the dynamic nature of battery performance over time.

Purpose of the Study:

• To develop an Inverse Design and Optimization Procedure (IDOP) for enhancing LIB lifetime prediction and design.
• To integrate Parameter Sensitivity Analysis (PSA) and Multiobjective Optimization (MOO) into a unified framework.

Main Methods:

• Developed a mechanism-driven LIB lifetime prediction model based on capacity degradation.
• Employed PSA to identify critical design parameters influencing battery lifespan.
• Utilized MOO to optimize direct (anode, electrolyte) and indirect (interfacial) characteristics.

Main Results:

• Parameter Sensitivity Analysis identified areal density, particle radius, and interface characteristics as key factors for battery lifetime.
• Multiobjective Optimization predicted potential battery lifetime extensions of up to 26.63% (at 25 °C) and 32.76% (at 45 °C) by optimizing direct factors.
• Further optimization of indirect factors showed even greater lifetime extensions: 70.97% (at 25 °C) and 138.41% (at 45 °C).
• Optimized parameters demonstrated excellent agreement with capacity degradation trajectories in the failure-mechanism-driven model.

Conclusions:

• The IDOP framework effectively improves the efficiency of LIB design and optimization.
• This approach offers a promising pathway for developing next-generation LIBs with extended operational lifespans.
• Understanding and optimizing interfacial characteristics are crucial for maximizing battery performance and longevity.