Electrode SOC and SOH estimation with electrode-level ECMs

TL;DR

This paper introduces a novel electrode-level ECM approach for estimating battery internal states like SOC and SOH, enabling better degradation prediction and health management in batteries.

Contribution

It presents a new electrode-level ECM method for estimating electrode states and updating SOH parameters, improving battery degradation prediction and health diagnosis.

Findings

Validated in simulation and experiments

Accurate electrode-level state estimation achieved

Enhanced degradation mode detection possible

Abstract

Being able to predict battery internal states that are related to battery degradation is a key aspect to improve battery lifetime and performance, enhancing cleaner electric transportation and energy generation. However, most present battery management systems (BMSs) use equivalent-circuit models (ECMs) for state of charge (SOC) and state of health (SOH) estimation. These models are not able to predict these aging-related variables, and therefore, they cannot be used to limit battery degradation. In this paper, we propose a method for electrode-level SOC (eSOC) and electrode-level SOH (eSOH) estimation using an electrode-level ECM (eECM). The method can produce estimates of the states of lithiation (SOL) of both electrodes and update the eSOH parameters to maintain estimation accuracy through the lifetime of the battery. Furthermore, the eSOH parameter estimates are used to obtain degradation mode information, which could be used to improve state estimation, health diagnosis and prognosis. The method was validated in simulation and experimentally.