Lithium-ion point-of-care ultrasound battery joint state of charge estimation

• Published in: Scientific African Vol. 24; p. e02232
• Main Authors: Ndzana, Nicolas Daniel Mbele, Nelem, Aristide Tolok, Abanda, Yannick Antoine, Pesdjock, Mathieu Jean Pierre, Ngouagna, Murele Vanina Toukam, Zeh, Odile Fernande, Ele, Pierre
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024; Elsevier
• Subjects: Battery management system; Battery model; Join state-of-charge estimation; Online identification; Point-of-care ultrasound system (POCUS); Battery model; Point-of-care ultrasound system (POCUS); Battery management system; Online identification; Join state-of-charge estimation
• ISSN: 2468-2276; 2468-2276
• DOI: 10.1016/j.sciaf.2024.e02232

With recent advances in ultrasound technology, including accessibility and portability, point-of-care ultrasound systems (POCUS) are becoming ubiquitous in urban and rural health services. The efficient use of POCUS battery power is necessary due to the limited access to electricity in rural locations. Accurate estimation of the state-of-charge (SOC) of the onboard lithium-ion battery is crucial in ensuring the safe, efficient, and reliable operation of the battery management system (BMS). This research suggests a novel approach for striking a reasonable balance between the POCUS battery management systems computation time and SOC estimate accuracy. Given that the lithium-ion battery is a non-linear and time-varying system, it is modelled by a second-order RC equivalent circuit model (2-RC ECM). Real-time parametric identification is carried out using the method of recursive least squares with respect to an adaptive forgetting factor (AFFRLS). The joint SOC evaluation technique, ordering the results of real-time identification and the Extended Kalman Filter (EKF) recommended for estimating battery SOC at temperatures between 0 and 45 °C. Simulations were carried out using experimental data from the DST (Dynamic Stress Test), incremental current and, the Federal Urban Driving Program (FUDS) of the CALCE research group to evaluate the proposed approach. Based on the monitoring of dynamic changes in model parameters and the computational complexity of existing algorithms, the results reveal that the joint AFFRLS-EKF estimation proposed for the SOC strategy achieves good accuracy, high adaptability and rapid convergence of SOC estimation.