{
  "$type": "site.standard.document",
  "description": "Battery node diagnostic data may be received from a battery system. One or more outlier detection machine learning models may be selected based on profile information included in the battery node diagnostic data. The profile information may identify a battery node operation profile associated with…",
  "path": "/patents/1370391",
  "publishedAt": "2024-10-24T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60L58/16",
    "Element Energy, Inc."
  ],
  "textContent": "Battery node diagnostic data may be received from a battery system. One or more outlier detection machine learning models may be selected based on profile information included in the battery node diagnostic data. The profile information may identify a battery node operation profile associated with some or all of the battery node diagnostic data. One or more of the battery nodes may be identified as outliers by applying the one or more outlier detection machine learning models to identify one or more differences between first diagnostic data for the designated subset of the battery nodes and a population-level representation of the battery node diagnostic data. Outcome values may be determined by applying one or more predetermined rules to the battery node diagnostic data. A battery node may be identified as exhibiting a fault based on the designated subset of the battery nodes and the plurality of outcome values.",
  "title": "ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ARCHITECTURE IN A BATTERY SYSTEM"
}