Open circuit voltage (OCV) has a considerable influence on the accuracy of battery state of charge (SOC) estimation. Three efforts have been made to reconstruct OCV for SOC estimation of lithium ion batteries in this study: (1) A new parameter backtracking strategy is proposed for online parameter identification using the recursive least square (RLS) algorithm to obtain stable OCV, which significantly reduces the jitters occurring in OCV identification results. (2) Historical experimental data of lithium ion batteries are used to derive baseline OCV curve and determine constraint boundaries, then an extended Kalman filter (EKF) is employed as a state observer to estimate the SOC for the same types of the batteries that have not been tested. (3) The OCV-SOC curve is reconstructed based on the accumulated online parameter identification and SOC estimation results. The OCV curve can be locally reconstructed even when the accumulated data only cover a partial range of SOC, which is suitable for electric vehicle (EV) operation conditions. Once the OCV curve is reconstructed, the response surface model of OCV-SOC-Capacity is applied to update battery capacity. In this way, the OCV curve can be gradually reconstructed from high SOC to low SOC during battery discharging process. The use of the reconstructed OCV curve to estimate SOC significantly improves the SOC estimation accuracy with the maximum error less than 3% for EV operation conditions.