What is yailab?
yailab provides an AI-powered predictive maintenance platform specifically developed for batteries connected to the power grid. The primary goal is to optimize the performance and financial returns of these energy storage assets by maximizing lifetime revenue and decreasing degradation.
By employing data-driven AI algorithms, yailab delivers real-time tracking of State-of-Health (SOH) degradation for individual battery units. This allows for precise degradation estimation under actual operating conditions, considering factors like current intensity, temperature fluctuations, and power demands, rather than relying on idealized laboratory models. The platform is designed to be chemistry-agnostic, supporting various battery types, and helps users make optimal selections of battery assets.
Features
- AI-Powered Predictive Maintenance: Utilizes AI algorithms for battery degradation estimation.
- Real-time SOH Tracking: Monitors State-of-Health degradation continuously for optimal asset selection.
- Chemistry Agnostic: Adaptable solution for various current and future battery technologies.
- Real-world Condition Modeling: Accounts for actual operating conditions (current, surges, temperature, usage modes).
- Individualized Asset Estimation: Provides specific degradation insights for each battery asset.
- Profitability Maximization: Aims to increase battery lifetime revenue by up to 50%.
- Lifetime Extension: Designed to decrease battery degradation by up to 30%.
Use Cases
- Optimizing battery asset management for energy companies.
- Predicting maintenance needs for grid-connected batteries.
- Maximizing revenue from battery energy storage systems.
- Extending the operational lifespan of battery installations.
- Tracking degradation for diverse battery chemistries in real-world scenarios.
- Informing trading decisions based on battery health.
