Lithium battery health condition multi-dimensional diagnosis method and system

By preprocessing lithium battery BMS data and reconstructing virtual ICA curves, a confidence spectrum is generated, which solves the accuracy problem of lithium battery health diagnosis under real driving conditions and realizes multi-dimensional battery health status assessment.

CN121784549APending Publication Date: 2026-04-03ZHEJIANG SHANGJIA MACHINERY
0 Cites 1 Cited by

Patent Information

Application Number
CN202511805909.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-03
Publication Date
2026-04-03

AI Technical Summary

Technical Problem

Existing lithium battery health diagnostic methods suffer from insufficient accuracy and reliability under real-world driving conditions due to incomplete data and non-constant current charging processes, making it difficult to accurately reflect the battery's health status.

Method used

By acquiring raw BMS data, extracting charging data segments and preprocessing them, reconstructing virtual ICA curves and confidence maps, extracting health features based on confidence weighting, and generating multi-dimensional SOH reports.

Benefits of technology

It effectively overcomes the interference of poor data quality on diagnostic accuracy under real driving conditions, significantly improves the practical applicability and reliability of diagnosis, and realizes multi-dimensional and accurate diagnosis of battery health status.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a lithium battery health condition multi-dimensional diagnosis method and system, and relates to the field of lithium battery health diagnos.The lithium battery health condition multi-dimensional diagnosis method comprises the steps that discrete charging data fragments are obtained from a vehicle management system, and intelligent reconstruction of a virtual incremental capacity analysis curve is conducted based on the fragments; a confidence map is synchronously generated to quantify the reliability of the reconstructed data. And then, key health features are extracted from the reconstruction curve, and adaptive weighting is performed on the features by using a confidence map, so that a comprehensive index vector capable of accurately reflecting the real aging state in the battery is generated. And finally, deep diagnosis is carried out based on the index vector, and multi-dimensional accurate diagnosis of the health condition of the battery is realized, so that a multi-dimensional evaluation report of the health condition of the battery is output. Therefore, the interference of poor data quality on the diagnosis precision under the real driving working condition can be effectively overcome, and the practical applicability and reliability of diagnosis are remarkably improved.
Need to check novelty before this filing date? Find Prior Art