In order to solve the environmental pollution and energy crisis, Electric Vehicles (EVs) have been developed rapidly. Lithium-ion (Li-ion) battery is the key power supply equipment for EVs, and the scientific and accurate prediction of its Remaining Useful Life (RUL) has become a hot topic in the field of new energy research. The internal resistance and capacity are often used to characterize the Li-ion battery State of Health (SOH) from which RUL is obtained. However, in practical applications, it is difficult to obtain internal resistance and capacity information by using the non-intrusive measurement method. Therefore, it is necessary to extract the measurable parameters to characterize the degradation of Li-ion battery. At present, the methods of extracting health indicators based on measurable parameters have gained preliminary results, but most of them are derived from the Li-ion battery discharging data.In this paper, a novel indirect Health Indicator (HI) is extracted from the charging data to quantify Li-ion battery fading. In particular, the Box-Cox transformation is adopted to improve the correlation between the extracted HI and the capacity, which is then quantified by the Pearson and Spearman correlation analysis. Furthermore, Extreme Learning Machine (ELM) is proposed to predict the Li-ion battery RUL based on the novel HI. The forecast results validate the effectiveness and efficiency of our presented method in Li-ion battery RUL prognostic and degradation modeling.