Engineering topics

Gering, Kevin L.

Publications and source records attributed to Gering, Kevin L..

3 records

DOE OSTI2025

Fast-charging lithium-ion batteries: Synergy of carbon nanotubes and laser ablation

Advancing lithium-ion battery (LiB) technology to achieve 10–15-min extreme fast charging (XFC) while maintaining high energy density and longevity poses a significant challenge. Addressing Li-plating is crucial, as it depletes useable Li, causing deterioration and safety issues. Here, this study explores a holistic approach incorporating Single-Wall Carbon Nanotubes (SWCNTs) and Laser Ablation (LA) to mitigate Li-plating while maintaining high charge acceptance under 10–15-min XFC. SWCNTs enhance the electrical conductivity and mechanical integrity of the positive electrode (PE), reducing overall cell overpotential at high charging rates. Concurrently, LA is applied to negative electrodes (NE) to reduce tortuosity of ion-diffusion pathways and increase surface wettability, improving Li-ion transport. Combining SWCNTs in the PE and LA on the NE, our experimental findings demonstrate a significant reduction in Li-plating and maintained high charge acceptance of ~84.33 % after 800 5C (12 min) charge cycles for cells having PE with ~3.3 mAh cm –2 and NE with 3.9 mAh cm –2 loadings. This study highlights the potential of combining SWCNTs and LA to address Li-plating in LiBs and opens new avenues for designing battery systems capable of achieving 10–15-min XFC.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Systems and methods for managing energy storage operations

An energy storage device (ESD) manager determines charge conditions that result in charge-related aging of an energy storage device (ESD), such as a battery, cell, or the like. The ESD manager may determine charge-related costs for charge operations, which may quantify charge-related aging imposed by subjecting ESD to specified charge conditions. The ESD manager may evaluate and/or modify charge operations to reduce charge-related aging. The ESD manager may be further configured to model charge-related aging behavior over time and/or under variable charge conditions. The ESD manager may configure charge operations to ensure that charge-related performance loss remains below a threshold for a specified usage duration.

Gering, Kevin L.↗

DOE OSTI2023

Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning

Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. Finally, the framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models.

25 ENERGY STORAGE↗