Session Overview (Day 1 - Degradation Prediction in Li-ion Batteries):

Degradation prediction in Li-ion batteries is a challenging and highly nonlinear problem. Traditional physics based approaches and tools need several design, electrochemical and degradation parameters and long computation times which make this approach expensive. Pure machine learning requires huge amounts of data and using real life drive cycles is still not possible. During this session, we will explain how a hybrid approach can be used to perform accurate capacity fade modeling at a fraction of the time and cost of traditional methods providing you both speed and accuracy.

Session Overview (Day 2 - Opportunities in Repurposing of EV batteries):

After a battery’s first life in a car and before it is recycled, it can be reused, refurbished, and repurposed. If the battery isn’t damaged during its use in an EV, such as in a car accident, these batteries have additional usable capacity – an estimated 80% of the original rated capacity. This means that if the battery was manufactured to store 100 kWh, it can now store up to 80 kWh. In order to make use of the remaining capacity, the batteries can be broken down to salvage smaller components for reuse and refurbishment, or they can be repurposed and used in a less demanding application, such as stationary storage. During this session, we will explain how this technology works and what are the business use cases around the same.