Battery Analytics for Energy Systems

Battery analytics refers to the use of data, models, and algorithms to monitor, predict, and optimize the performance and lifecycle of batteries. It enables organizations to improve safety, reliability, and return on investment.

A BMS ensures batteries operate safely day-to-day, while battery analytics provides deeper insights using advanced modeling and AI to predict degradation, optimize usage, and plan maintenance across the entire lifecycle.

Digital twins provide a continuous thread of data across design, manufacturing, and operations, ensuring compliance and lifecycle intelligence. Explore more in our lifecycle intelligence article.

Peaxy Innovate provides battery modeling software and cell chemistry simulation tools that help researchers design and test batteries virtually, reducing the need for costly physical prototypes.

A LIMS solution centralizes experimental data, streamlines workflows, and improves reproducibility in materials science. Learn more in our LIMS article.

Peaxy Build uses battery manufacturing analytics and quality control data to detect defects, reduce scrap, and increase yield in pilot and gigafactory production lines.

Data-driven decision making helps manufacturers reduce costs, improve efficiency, and accelerate time to market. See examples in our article on data-driven manufacturing.

A digital BOM provides traceability, reduces errors, and lowers costs through better visibility into component sourcing and usage. Explore more in our article on digitized BOMs.

Peaxy Operate offers real-time battery performance monitoring and predictive battery maintenance, helping operators extend asset life, improve uptime, and manage risk across fleets.

Regular battery health checks identify early signs of degradation, reduce downtime, and extend the usable life of assets. Learn more in our battery health checks article.

Peaxy Predict uses battery degradation modeling and battery life prediction software to forecast end-of-life, reduce warranty claims, and optimize the total cost of ownership for energy storage assets.

Digital twins create a virtual representation of battery systems to simulate performance and predict outcomes before making costly decisions. See how in our digital twins article.