A simple approach for online estimation of state-of-health of lithium-ion battery pack

Determination of State-of-health (SoH) is essential for monitoring the aging of batteries in electric vehicles. This paper proposes an online SoH estimation algorithm that utilizes threshold state-of-charge (TSP) data points collected during charging. The algorithm first establishes a linear relationship between TSPs and battery capacity through initial offline testing. This relationship is then integrated into the battery management system (BMS) of the EV, enabling real-time calculation of battery capacity during charging (measurand). The SoH is subsequently estimated from the computed capacity. The performance of the proposed algorithm is demonstrated for the NMC chemistry (using the nasa dataset) and the LFP chemistry (using the Mendeley dataset). The proposed algorithm is further evaluated experimentally on a 3 . 7 V , 2 . 2 Ah , NMC type Li-ion cell. The maximum absolute estimation residuals obtained from the validation cases of nasa , Mendeley, and the experimental dataset are 3.2%, 1.25%, and 1.13%, respectively. • This work reports a linear regression method for State-of-Health (SoH) estimation. • A novel health indicator, threshold SoC point (TSP), is defined during charging. • The developed TSP is used during runtime to estimate the battery capacity and SoH. • A laboratory setup has been developed to test and validate the proposed algorithm.