Diagnosis of the open-circuit voltage curve of batteries with voltage-controlled models

The open-circuit voltage curve (OCV curve) is a fundamental property of batteries. It is a signature of the battery chemistry and contains details on health and degradation modes. This article presents a novel method for operando diagnosis of the OCV curve using a voltage-controlled model (VCM). The method is based on a dynamic equivalent circuit model which uses measured voltage as input and simulates current as output. Theory and algorithm are developed for estimating the OCV curve from the difference between experimental and simulated current. The algorithm is demonstrated with four different lithium-ion battery cells that have different chemistries (metal-oxide and iron-phosphate positive electrodes), formats (pouch and prismatic), capacities (between 0.35 Ah and 180 Ah) and states of health (fresh and aged). Different operating protocols were used, spanning the range from 2-h single-cycle data up to 60-h multi-rate data and including dynamic and partial cycles. In all cases, the algorithm was able to successfully estimate the OCV curve. Mean absolute errors between estimated and true OCV curves were below 20 mV for each protocol and 10.1 mV in average. The algorithm thus allows to identify battery chemistries and to differentiate between fresh and aged batteries, without requiring low-current pseudo-OCV measurements. • New algorithm estimates the battery OCV curve using a voltage-controlled model. • Arbitrary protocols with dynamic voltage and current data possible. • Validated on four diverse lithium-ion cells (chemistry, format, capacity, age). • High accuracy achieved: Mean absolute error below 20 mV (average 10.1 mV). • Allows OCV estimation without needing slow, low-current pseudo-OCV measurements.