NASA Battery Health Monitoring Data Using Bayesian Methods

Experimental data from Li-ion batteries used to validate an integrated Bayesian approach for battery health monitoring. The approach uses Relevance Vector Machines and Particle Filters to estimate state-of-charge, state-of-health, and state-of-life. The dataset is provided by the National Aeronautics and Space Administration and was last updated in April 2026.

Use Cases

• Train models for battery state-of-charge (SOC) estimation based on indirect measurements and operational conditions.
• Develop prognostic algorithms for battery state-of-health (SOH) and state-of-life (SOL) using Bayesian regression and classification.
• Validate particle filter frameworks for state estimation in complex electrochemical systems.
• Benchmark relevance vector machine (RVM) performance against other methods for battery diagnosis.

Strengths

• Dataset is from a validated experimental approach presented in the description.
• Focuses on critical metrics for battery management: SOC, SOH, and SOL.
• Provided by the authoritative organization National Aeronautics and Space Administration.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is stored in a BIN format, which may require specialized tools for access.

Provenance

Source

National Aeronautics and Space Administration

Collection Method

Experimental data from Li-ion batteries, as mentioned in the description.

Time Range

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Freshness

Last updated 2026-04-10 19:38:10.359287; freshness should be verified.

Geography

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