Interview with Antonio Silvio de Letteriis (FLASHBATTERY)

Interview with Antonio Silvio de Letteriis

The dataset shared with the Fastest consortium comprises 7,000 batteries and 1 million records.

1. How does FLASHBATTERY’s expertise and knowledge enhance the functionality of a hybrid testing platform, particularly in optimizing resource scheduling and improving efficiency?

Flash Battery’s extensive experience in battery production, equipped with remote control and a system of data collection, allowed us to provide essential skills and the database required to support a hybrid testing platform design.

Through our technical expertise, we have efficiently modeled effective techniques for optimizing resource scheduling and improving system efficiency.

2. In what ways does the algorithm design, with FLASHBATTERY’s involvement, enhance the battery investigation environment, particularly in terms of streamlining processes and increasing accuracy?

Flash Battery has enhanced the battery investigation process by sharing testing procedures that adhere to standards related to off-road and industrial applications with Fastest partners.

The extensive range of applications and the large amount of data collected during our battery tests and real-world usage enabled us to pinpoint measures for streamlining the process and boosting the precision of the algorithm.

The dataset shared with the Fastest consortium comprises 7,000 batteries and 1 million records. We have chosen to share a real-world battery usage dataset via a cloud platform, aiming to ensure adequate accessibility and performance for all partners.

 

3. What do you think are the aspects that will be crucial to investigate in the following years to improve batteries and making them more efficient and reliable as well as less resource-consuming?

By investing in the development of advanced battery control and management systems, we use cutting-edge software technologies to reduce system inefficiencies and boost reliability. This is achieved by continuously monitoring the system’s State of Health (SOH) and employing predictive diagnostics to manage spare parts and address issues. Such measures streamline planning processes and lessen the environmental footprint of maintenance activities.