Capacity and Resistance Diagnosis of Batteries with Voltage-Controlled Models

Abstract

Capacity and internal resistance are key properties of batteries determining energy content and power capability. We present a novel algorithm for estimating the absolute values of capacity and internal resistance from voltage and current data. The algorithm is based on voltage-controlled models. Experimentally-measured voltage is used as an input variable to an equivalent circuit model. The simulation gives current as output, which is compared to the experimentally-measured current. We show that capacity loss and resistance increase lead to characteristic fingerprints in the current output of the simulation. In order to exploit these fingerprints, a theory is developed for calculating capacity and resistance from the difference between simulated and measured current. The findings are cast into an algorithm for operando diagnosis of batteries operated with arbitrary load profiles. The algorithm is demonstrated using cycling data from lithium-ion pouch cells operated on full cycles, shallow cycles, and dynamic cycles typical for electric vehicles. Capacity and internal resistance of a “fresh” cell was estimated with high accuracy (mean absolute errors of 0.9% and 1.8%, respectively). For an “aged” cell, the algorithm required adaptation of the model’s open-circuit voltage curve to obtain high accuracies. Highlights Operando diagnosis of capacity and internal resistance of rechargeable batteries. Novel algorithm developed, validated and demonstrated. Use of voltage-controlled models: Voltage as input, current as output. High accuracy achieved for dynamic operation of an NMC-LMO/graphite pouch cell.