Tsai, MRMRTsaiKUO-CHING CHEN2023-02-032023-02-0320232352-152Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/627598Accurately estimating the state of a battery is crucial to its safety and remaining life prediction. This paper proposes a mathematical treatment, which enables us to estimate both the state of charge (SOC) and the state of health (SOH) of each battery in a pack. The charge voltage curve (CVC) of a fresh single battery, charged at 0.2C rate, is numerically fitted by a polynomial function. Since the internal resistance of a battery varies with the charging current and the SOH, the current-interruption method is adopted to determine the amount of the translation and scaling of the reference CVC under different conditions. Our experiment confirms that the generalized CVC can be successfully applied to various scenarios of different charge rates and SOHs. With the CVC of a battery, the adaptive moment estimation, accompanied by a 30-min (or 45-min) charge voltage data, is employed to find out the current SOC and SOH of one single battery or each of the battery in a series pack. The experiment in nine different cases demonstrates that the estimation of the current SOC and SOH by this CVC approach is excellent and the maximum error could be lower than 3.6 %.Lithium-ion battery; Charge voltage curve; State of charge; State of health; Adaptive moment estimation; HEALTH ESTIMATION; ONLINE EQUALIZATION; BOARD STATE; CELL; PACK[SDGs]SDG7[SDGs]SDG11[SDGs]SDG12journal article10.1016/j.est.2022.1065022-s2.0-85145596137WOS:000910621600001https://api.elsevier.com/content/abstract/scopus_id/85145596137