Lithium-ion battery degradation: Introducing the concept of reservoirs to design for lifetime

Abstract

Designing lithium-ion batteries for long service life remains a challenge, as most cells are optimized for beginning-of-life metrics such as energy density, often overlooking how design and operating conditions shape degradation. This work introduces a degradation-aware design framework built around finite, interacting reservoirs (lithium, porosity, and electrolyte) that are depleted over time by coupled degradation processes. We extend a physics-based Doyle-Fuller-Newman model to include validated mechanisms such as SEI growth, lithium plating, cracking, and solvent dry-out, and simulate how small design changes impact lifetime. Across more than 1,000 cycles, we find that increasing electrolyte volume by just 1% or porosity by 5% can extend service life by over 30% without significantly affecting cell energy density. However, lithium excess, while boosting initial capacity, can accelerate failure if not supported by sufficient structural or ionic buffers. Importantly, we show that interaction between reservoirs is crucial to optimal design: multi-reservoir tuning yields either synergistic benefits or compound failures, depending on operating conditions. We also quantify how C-rate and operating temperature influence degradation pathways, emphasizing the need for co-optimized design and usage profiles. By reframing degradation as a problem of managing finite internal reservoirs, this work offers a predictive and mechanistic foundation for designing lithium-ion batteries that balance energy, durability, and application-specific needs.

Figures (5)

• Figure 1: Effect of lithium reservoir (a) State-of-health (SoH) versus charge throughput for four lithium reservoir configurations: standard cell (5.00 Ah), +0.26 Ah Li (N/P = 0.82), ${-}0.26$ Ah Li (N/P = 0.75), and +0.26 Ah Li with adjusted positive electrode to retain N/P = 0.78. Service life is defined as the point at which SoH drops to 80%. (b) Degradation mode breakdown at the 5-year mark (18.25 kAh), showing total loss of lithium inventory (LLI), loss of active material in the negative and positive electrodes (LAM_NE, LAM_PE), and their mechanistic contributors: LAM from cracking and dry-out, and LLI from SEI formation, lithium plating, and active material isolation.
• Figure 2: Effect of porosity reservoir tuning on battery performance. (a) State-of-health (SoH) versus charge throughput for varying electrode thickness ratios ($R_{thickness}$= 0.95, 1.00, 1.05). (b) Degradation mode breakdown at the 5-year mark (18.25 kAh), showing LLI and LAM contributions from SEI, plating, cracking, dry-out, and active material isolation.
• Figure 3: Effect of electrolyte reservoir size on cell performance and degradation. (a) State-of-health (SoH) versus charge throughput for cells with varying electrolyte excess ratios ($R_{excess}$ = 0.99, 1.00, 1.01). (b) Degradation breakdown at the 5-year mark (18.25 kAh), showing contributions to loss of lithium inventory (LLI) and loss of active material in the negative and positive electrodes (LAM_NE, LAM_PE), including mechanisms such as SEI formation, plating, and dry-out.
• Figure 4: Relative improvement in service life and energy density for various multi-reservoir configurations, expressed as a ratio to the standard cell. Each bar represents a unique combination of lithium inventory (L), porosity (P), and electrolyte volume (E), with ‘+’ and ‘-’ indicating increases or decreases from baseline values. Left panel shows service life improvement; middle and right panels show corresponding changes in gravimetric and volumetric energy density.
• Figure 5: Service life at 80% state of health (SoH) under varying reservoir sizes, coupled reservoir configurations, operating conditions, and their combined effects.