Data-Driven State of Health Estimation of Li-Ion Batteries with RPT-Reduced Experimental Data

Abstract

In order to accurately estimate the state of health (SOH) of a Li-ion battery, a reference performance test (RPT) needs to be conducted between several charging/discharging cycles for obtaining accurate data concerning the battery capacity and degradation. However, it is not practical to perform RPTs frequently because they are time-consuming and expensive; moreover, the Li-ion battery undergoes unnecessary degradation during test operations. Therefore, the RPTs should be performed as infrequently as possible. In this paper, a neural network-based SOH estimation scheme with reduced experimental data measured by the RPT is proposed for achieving economic efficiency and mitigating the dispensable degradation being caused by additional experiments. For the RPT-reduced experimental data, the continuous SOH estimation problem is formulated into a classification problem. The neural network learns how to estimate the SOH values using short time-series voltage and current data, labeled as the corresponding SOH values by the RPTs. Even in the SOH regions, where the data are not labeled as any given class and there is no prior knowledge on the corresponding SOH, the proposed SOH estimation scheme works well by performing regression with the class probability distribution.