Justin Ochoa, Texas A&M University – Kingsville

An Efficient On-Board Health Monitoring for Multicell Lithium-Ion Battery Systems using Gaussian Process Clustering

Abstract: Accurately monitoring health condition of individual lithium-ion (Li-ion) cells such as state of charge (SOC) and state of health (SOH) in multicell batteries is crucial to build high-performance and safety-critical battery systems. However, this involves considerable computational burden to embedded battery management systems (BMSs), as number of battery cells increases. This project proposes an efficient on-board health monitoring method for multicell Li-ion battery systems consisting of large number of cells using the proposed Gaussian process clustering. The Gaussian process clustering method first preprocesses all cell voltages using Z-score standardization and classifies a normal cell cluster and an abnormal cell cluster using outlier scoring vector. Then, a representative of the normal cell cluster and abnormal cell cluster are carefully monitored using the threads of a condition monitoring algorithm. Instead of using cell-based method for all cells in a pack, the proposed method can significantly reduce the computation cost for onboard implementation.

Presentation Author(s):
Justin Ochoa*

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