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 “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 NMC lithium-ion cells. Both proposed methods exhibit a high accuracy (0.63% and 1.37% RMSE 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.
Online Li-ion Battery State of Health Estimation during Electric Vehicles Fast Charging: A Comparative Analysis of Advanced Data-Driven Techniques
C. Scarpelli;F. Quilici;G. Lutzemberger;
2026-01-01
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 “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 NMC lithium-ion cells. Both proposed methods exhibit a high accuracy (0.63% and 1.37% RMSE 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
