Electrochemical degradation in lithium-ion batteries is a critical factor affecting performance, safety, and longevity. Root cause analysis of such degradation involves identifying and understanding mechanisms like lithium plating, cathode cracking, and solid-electrolyte interphase (SEI) layer growth. These phenomena contribute to capacity fade, increased internal resistance, and eventual cell failure. Advanced diagnostic tools, including electrochemical impedance spectroscopy (EIS) and differential voltage analysis (DVA), are essential for isolating these failure modes. Insights from these analyses feed into aging models to predict long-term behavior, enabling better battery design and management strategies.

Lithium plating occurs when lithium ions are reduced to metallic lithium on the anode surface instead of intercalating into the graphite structure. This typically happens under high charging rates, low temperatures, or overcharging conditions. Plating leads to irreversible capacity loss and can create dendrites that puncture the separator, causing internal short circuits. EIS helps detect lithium plating by identifying changes in the anode's charge-transfer resistance. DVA provides additional insights by revealing voltage plateaus associated with lithium stripping and plating reactions. Research shows that plating is more severe in cells with high-energy-density designs, where anode porosity and electrolyte transport properties are limiting factors.

Cathode cracking results from mechanical stress induced by repeated volume changes during charge-discharge cycles. Layered oxide cathodes, such as NMC (nickel-manganese-cobalt), experience anisotropic expansion and contraction, leading to particle fracture. This cracking exposes fresh surfaces to the electrolyte, accelerating parasitic reactions and increasing impedance. SEM and XRD are often used to observe microstructural damage, while DVA can track shifts in cathode redox peaks due to active material loss. Studies indicate that higher nickel content in cathodes exacerbates cracking due to larger volume changes, though advanced coatings and doping strategies can mitigate this.

SEI layer growth is a natural process that stabilizes the anode-electrolyte interface but becomes problematic when excessive. The SEI forms through electrolyte reduction reactions, consuming lithium ions and increasing resistance over time. While a thin, stable SEI is beneficial, uncontrolled growth leads to capacity fade. EIS is particularly effective for monitoring SEI evolution, as it can distinguish between resistive and capacitive components of the interface. DVA complements this by quantifying lithium inventory loss tied to SEI formation. Temperature plays a significant role, with SEI growth accelerating at elevated temperatures due to faster reaction kinetics.

Electrochemical impedance spectroscopy is a powerful tool for deconvoluting degradation mechanisms. By applying a small AC signal across a range of frequencies, EIS measures the cell's impedance response, which can be modeled using equivalent circuits. Key parameters include ohmic resistance, charge-transfer resistance, and Warburg impedance, each linked to specific degradation modes. For example, increased charge-transfer resistance often indicates lithium plating or SEI growth, while Warburg impedance changes may reflect cathode cracking. EIS data must be interpreted carefully, as overlapping phenomena can obscure individual contributions.

Differential voltage analysis offers another perspective by examining the voltage response during slow, incremental charge or discharge. The derivative of voltage with respect to capacity reveals peaks corresponding to phase transitions in electrode materials. Shifts or attenuation of these peaks indicate active material loss or lithium inventory depletion. DVA is especially useful for identifying lithium plating, which appears as an additional peak at low voltages. Combining DVA with coulombic efficiency measurements provides a more complete picture of degradation pathways.

These diagnostic techniques feed into aging models that simulate long-term battery behavior. Physics-based models incorporate degradation mechanisms like SEI growth and lithium plating, while empirical models rely on accelerated aging data. Hybrid approaches leverage machine learning to improve prediction accuracy. A well-parameterized model can separate the effects of different degradation modes, enabling targeted improvements in cell design or operating protocols. For instance, if SEI growth is identified as the dominant aging factor, electrolyte additives or temperature management strategies can be prioritized.

The interplay between degradation mechanisms complicates root cause analysis. Lithium plating may accelerate SEI growth by increasing surface area, while cathode cracking can exacerbate electrolyte oxidation. Multi-modal characterization is often necessary to disentangle these effects. Advanced techniques like operando XRD or neutron diffraction provide real-time insights into structural changes, though they are less accessible than EIS or DVA.

Mitigation strategies depend on the identified root causes. For lithium plating, optimizing charging protocols and anode design is critical. Cathode cracking can be reduced through particle morphology engineering or stress-relieving binders. SEI growth is managed via electrolyte formulations that promote stable interfaces. Each solution must balance performance, cost, and safety considerations.

Root cause analysis is not a one-time activity but an ongoing process as battery technologies evolve. New materials, such as silicon anodes or solid-state electrolytes, introduce unique degradation challenges that require updated diagnostic approaches. Collaboration between experimentalists and modelers is essential to keep pace with these developments.

In summary, electrochemical degradation in lithium-ion batteries arises from complex, interrelated mechanisms. Tools like EIS and DVA enable precise identification of root causes, informing both immediate corrective actions and long-term aging models. By understanding and addressing these degradation pathways, researchers and engineers can develop more durable and reliable energy storage systems.