Byterat

Byterat ingests high-frequency electrochemical time-series data from heterogeneous battery testing equipment (potentiostats, cyclers, thermal chambers) and normalizes it into a standardized internal schema that preserves electrochemical context (voltage, current, temperature, impedance, cycle count). The platform uses equipment-specific parsers and metadata extraction to automatically detect data provenance, sampling rates, and measurement units, then maps them to a canonical data model that enables cross-equipment analysis without manual preprocessing.

Byterat performs automated degradation analysis by tracking multiple performance metrics (capacity fade, resistance growth, voltage hysteresis, cycle efficiency) across test cycles and correlating them with environmental conditions (temperature, humidity, state-of-charge windows). The platform uses statistical decomposition and curve-fitting algorithms to isolate degradation mechanisms (calendar aging vs. cycle aging, lithium plating, electrolyte decomposition) and projects remaining useful life (RUL) based on fitted degradation curves and empirical failure thresholds.

Byterat provides a web-based dashboard for exploring battery test data across multiple dimensions simultaneously — voltage/current/temperature profiles, cycle-by-cycle capacity trends, Nyquist impedance plots, and environmental correlations. The visualization engine uses interactive filtering, cross-linked plots, and drill-down navigation to enable researchers to identify patterns (e.g., capacity loss acceleration at high temperatures) without writing analysis code. The platform supports custom plot templates and allows users to overlay multiple test runs for comparative analysis.

Byterat defines and enforces a canonical data schema for battery testing that includes standardized field names, unit conventions, measurement uncertainty metadata, and hierarchical relationships (test → cycle → measurement). The platform maintains a metadata catalog that tracks data provenance (equipment model, calibration date, operator, test protocol), version history, and data quality flags. This schema enables cross-lab data sharing and automated analysis pipeline compatibility without manual schema negotiation.

Byterat automatically extracts cycle-level features (discharge capacity, charge capacity, round-trip efficiency, voltage hysteresis, impedance at specific states of charge) from raw time-series data and aggregates them into structured datasets suitable for machine learning or statistical analysis. The platform supports batch processing of thousands of cycles across multiple test runs and can compute derived metrics (capacity fade rate, efficiency loss per cycle, temperature-normalized degradation) without user-written code.

Byterat provides a multi-user workspace for organizing battery test campaigns, assigning roles and permissions, and sharing datasets with collaborators across organizations. The platform tracks who created, modified, or accessed each dataset, maintains audit logs for compliance, and supports granular access control (read-only, analysis, export permissions). Users can create shared analysis workspaces where multiple researchers can view the same visualizations and add annotations or comments without overwriting each other's work.

Byterat allows users to define analysis workflows as reusable protocols that specify a sequence of operations (data ingestion, normalization, feature extraction, degradation analysis, visualization) and can be applied to new test datasets automatically. Protocols are parameterized (e.g., failure threshold, degradation model type) and can be versioned, shared, and audited. When a new test dataset is uploaded, matching protocols can be triggered automatically to produce standardized analysis outputs without manual intervention.

Byterat provides a machine learning module that enables users to train predictive models (regression, classification, neural networks) on battery test data to predict outcomes like remaining useful life, failure probability, or optimal operating conditions. The platform handles data preprocessing, feature normalization, train/test splitting, hyperparameter tuning, and model evaluation without requiring users to write code. Trained models can be deployed for inference on new test data, with uncertainty quantification and feature importance analysis.

Byterat exposes a REST API that allows external applications and scripts to query battery test data, trigger analysis pipelines, retrieve results, and manage datasets programmatically. The API supports filtering by test parameters (cell chemistry, temperature range, cycle count), pagination for large result sets, and webhooks for event-driven automation (e.g., trigger analysis when a test completes). Authentication uses API keys or OAuth, and rate limiting prevents abuse.