The estimation of the state of charge (SOC) is crucial to determine the remaining capacity of the lithium-ion battery, and thus plays an important role in many electric vehicles and construction equipment driven by the lithium-ion battery. In this study, the optimal algorithm was investigated for high-voltage lithium-ion batteries of construction equipment that can be operated under harsh environmental conditions. The SOC estimation algorithms are designed by combining the Extended Kalman Filter which is an adaptive control method for nonlinear systems. The discharge capacity profiles was evaluated under each C-rate and various temperature to estimate SOC accurately. Also, the internal parameters required for the electrical equivalent circuit model were obtained by measuring the OCV and internal resistance under various temperature and C-rate conditions for the battery cell and battery module, respectively. The proposed SOC estimation algorithm was achieved excellent performance with maximum error of less than 5%. Finally, the SOC estimation method was found to satisfy the precision requirements in various temperature conditions.