Predictive analytics

Predictive analytics algorithms analyzes vast amounts of data from batteries to identify patterns and predict future performance, optimizing battery usage and prolonging battery life.

Hybrid approach: Machine learning + Equivalent circuit model

Data-driven machine learning algorithms combined with equivalent circuit models learn usage patterns to optimize battery performance and predict potential issues before they occur.

Real-time storage monitoring

Platform for near-real time battery analytics and monitoring to track battery performance and alert for preventable issues and warranty claim settlements.

Advanced data visualization

Advanced data visualization tools for battery designers and researchers to gain insights into battery performance and enable end users to make informed battery usage decisions.

Cloud and edge: hybrid architecture

A hybrid cloud and edge processing architectures to store, compress, transfer and analyze vast amounts of battery data, enabling scalable solutions to satisfy large organization fleet requirements.

Integration with IoT devices for workflows

Platform agnostic to data collection hardware with several integration for QuantaCloud API or Quanta-BMS. With configurable schema quantaflare battery cloud enables seamless battery data engine.