Program

Dr. Amit Gupta

Indian Institute of Technology, Delhi

Lithium-ion Batteries: From Portable Electronics to Drivetrains

In recent years, the successful development and implementation of high-energy Li-ion batteries has allowed for their use in a variety of portable devices. Li-based batteries offer higher gravimetric and volumetric energy densities, in addition to higher specific power.

Moreover, Li-ion batteries are less toxic as compared to lead-acid or Ni-Cd, and disposing them is less hazardous to the environment. Due to their light weight design (because Lithium is the most electropositive and the lightest metal) and reversible characteristics, Li-based batteries have become a rapidly evolving field of research for energy storage applications.

Although Li-ion offers considerable improvements over the conventional rechargeable batteries, it has its share of disadvantages as well. Li-metal based technologies can bechallenging to implement due to the irreversibility that could manifest as a result of Lithium reacting with the electrolyte. To attain longer cycle life and highly reversible reactions electrodes need to be manufactured out of materials that can enable a faster transfer of ionsfrom one electrode to another. Even after sustained developments, the extension of Li-ion batteries from portable electronics to hybrid and electric vehicles is yet to be achieved.

Developing a better understanding of the role of material properties and manipulating the morphology of the particle clusters comprising Li-ion electrodes could lead to potential opportunities for attaining higher performance goals. In this effect, the key feature highlighted in this talk is the modeling techniques being followed to analyze battery performance by studying processes at disparate time scales, including development of a multiscale model to gain fast-prediction of battery performance. The long-term objective is to employ such a framework to obtain a mathematical model to predict 'cell health' as a function of 'control' variables to predict cell aging, degradation aspects and performance for applications such as hybrid/electric vehicles.

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