Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data

TL;DR

This paper introduces a novel method to reconstruct and analyze open circuit voltage curves from multi-year home storage data, enabling degradation mode detection without laboratory tests.

Contribution

The study presents a continuous OCV curve reconstruction technique from operational data, allowing field-based degradation analysis of batteries over extended periods.

Findings

Loss of lithium inventory is the dominant degradation mode.

The method accurately tracks known laboratory features in field data.

Field capacity tests validate the reconstruction approach.

Abstract

A battery's open circuit voltage (OCV) curve can be seen as its electrochemical signature. Its shape and age-related shift provide information on aging processes and material composition on both electrodes. However, most OCV analyses have to be conducted in laboratories or specified field tests to ensure suitable data quality. Here, we present a method that reconstructs the OCV curve continuously over the lifetime of a battery using the operational data of home storage field measurements over eight years. We show that low-dynamic operational phases, such as the overnight household supply with electricity, are suitable for recreating quasi OCV curves. We apply incremental capacity analysis and differential voltage analysis and show that known features of interest from laboratory measurements can be tracked to determine degradation modes in field operation. The dominant degradation mode observed for the home storage systems under evaluation is the loss of lithium inventory, while the loss of active material might be present in some cases. We apply the method to lithium nickel manganese cobalt oxide (NMC), a blend of lithium manganese oxide (LMO) and NMC, and lithium iron phosphate (LFP) batteries. Field capacity tests validate the method.