{
  "$type": "site.standard.document",
  "coverImage": {
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  "description": "Techniques for machine learning-based degradation optimization are disclosed. In embodiments, a method includes identifying a power module, wherein the power module is controlled with a set of variables; determining, using functional relations of degradation of a degradation machine learning model…",
  "path": "/patents/1423456",
  "publishedAt": "2026-06-18T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "H01M8/04664",
    "C3.ai, Inc."
  ],
  "textContent": "Techniques for machine learning-based degradation optimization are disclosed. In embodiments, a method includes identifying a power module, wherein the power module is controlled with a set of variables; determining, using functional relations of degradation of a degradation machine learning model, optimal set-point values that minimize degradation of the power module while utilizing minimal resources; and reducing a degradation rate of the power module by adjusting one or more of the variables that control the power module based on the determined optimal set-point values.",
  "title": "MACHINE LEARNING-BASED DEGRADATION OPTIMIZATION FRAMEWORK"
}