Online Li-Ion Battery State-of-Health Estimation During Electric Vehicles Fast Charging: A Comparative Analysis of Advanced Data-Driven Techniques

Abstract

Online battery state-of-health (SoH) assessment is paramount to improving battery electric vehicles (BEVs) competitiveness. By continuously acquiring data from the on-board battery, it enables to implement strategies aiming to reduce degradation rate and enhance BEVs safety. This work presents the application and validation of two novel patented methodologies for lithium-ion battery SoH estimation, evaluated under realistic BEVs fast-charging operating profiles and benchmarked against experimental measurements. The proposed methods, i.e., the “OCV–SoC-based method” relying on the open-circuit voltage (OCV)–state-of-charge (SoC) characteristic curve and the “discrete wavelet transform (DWT)-based method” applying DWT analysis to in operando voltage signals, are compared with the widely adopted reference technique incremental capacity analysis (ICA). Performances of all applied methods are assessed with reference to experimental capacity fading data obtained from a dedicated cycling aging campaign performed on commercial nickel manganese cobalt (NMC) lithium-ion cells. Both proposed methods exhibit a high accuracy (0.63% and 1.37% root-mean-square-error values for “DWT-based method” and “OCV–SoC-based method,” respectively) comparable with the ICA one (0.98%). Pros and cons of the proposed methods are discussed, together with future activities needed to extend their application domain.