Faster charging and improved battery cycle life are critical to wider adoption of EVs. Physics-based models can be used for improved battery control and estimation. Slow computational times of solving have limited the use of physics-based models for real-time BMS applications. Manan will present strategies to perform model reformulation of physics-based porous electrode 1D and 2D models, that reduces computational times and can be solved on low-memory embedded platforms in a very fast manner. Development of model-based optimal charging profiles and experimental validation in improvement in cycle life and charge times on commercially relevant batteries will be presented. In collaboration with NREL, we have shown increase in cycle-life by more than 100%, as well as significant reduction in battery charge times. The impact of this technology on the transformation of electric vehicles, consumer electronics and grid storage market will also be discussed.

Takeaway

The presentation will be easy to follow and requires no prerequisites or battery knowledge/background to understand. Real life analogies of simpler systems compared to batteries will be presented and the talk will be interactive in nature. The audience will be able to appreciate the importance of optimal charging patterns and the need of physics-based model for real-time BMS applications.

Intended Audience

The talk is aimed for general audience, with no prerequisites required to attend or understand this technology/talk. The target customers for this technology however, are EV/OEM manufacturers, battery manufacturers, consumer electronics (cellphone/laptop) manufacturers, robots/drone manufacturers or electric airplane manufacturers.