Abstract—This paper describes the application of state-estimation\ud techniques for the real-time prediction of the state-of-charge\ud (SoC) and state-of-health (SoH) of lead-acid cells. Specifically,\ud approaches based on the well-known Kalman Filter (KF) and\ud Extended Kalman Filter (EKF), are presented, using a generic\ud cell model, to provide correction for offset, drift, and long-term\ud state divergence—an unfortunate feature of more traditional\ud coulomb-counting techniques. The underlying dynamic behavior\ud of each cell is modeled using two capacitors (bulk and surface) and\ud three resistors (terminal, surface, and end), from which the SoC\ud is determined from the voltage present on the bulk capacitor. Although\ud the structure of the model has been previously reported for\ud describing the characteristics of lithium-ion cells, here it is shown\ud to also provide an alternative to commonly employed models of\ud lead-acid cells when used in conjunction with a KF to estimate\ud SoC and an EKF to predict state-of-health (SoH). Measurements\ud using real-time road data are used to compare the performance\ud of conventional integration-based methods for estimating SoC\ud with those predicted from the presented state estimation schemes.\ud Results show that the proposed methodologies are superior to\ud more traditional techniques, with accuracy in determining the\ud SoC within 2% being demonstrated. Moreover, by accounting\ud for the nonlinearities present within the dynamic cell model, the\ud application of an EKF is shown to provide verifiable indications of\ud SoH of the cell pack
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.