Aging States Estimation and Monitoring Strategies of Li-Ion Batteries Using Incremental Capacity Analysis and Gaussian Process Regression

TL;DR

This study develops a data-efficient method using incremental capacity analysis and Gaussian process regression to accurately estimate battery health and remaining useful life with minimal diagnostic data, enabling safe operation from beginning to end of life.

Contribution

It introduces a novel approach combining ICA features with multi-model Gaussian process regression for precise SoH and RUL estimation using sparse data from multiple cells.

Findings

Achieved a normalized mean absolute error of 1.3% for SoH estimation.

Estimated RUL with a mean absolute error of 5.3%.

Only four diagnostic measurements are needed over a cell's lifetime.

Abstract

Existing approaches for battery health forecasting often rely on extensive cycling histories and continuously monitored cells. In contrast, many real-world scenarios provide only sparse information, e.g. a single diagnostic cycle. In our study, we investigate state of health (SoH)- and remaining useful life (RUL) estimation of previously unseen lithium-ion cells, relying on cycling data from begin of life (BOL) to end of life (EOL) of multiple similar cells by using the publicly available Oxford battery aging dataset. The estimator applies incremental capacity analysis (ICA)-based feature extraction in combination with data-efficient regression methods. Particular emphasis is placed on a multi-model Gaussian process regression ensemble approach (GPRn), which also provides uncertainty quantification. Due to a rather cell invariant behaviour, the mapping of ICA features to SoH estimation is highly precise and points out a normalized mean absolute error (NMAE) of 1.3%. The more cell variant mapping to RUL estimation is challenging, reflecting in a NMAE of 5.3%. Using the estimation results, a RUL monitoring strategy is derived. The objective is to safely operate a battery cell from BOL to EOL by only taking sparse diagnostic measurements. On average, only four diagnostic measurements are required during a cell's lifetime of 3300 to 5000 cycles.