Range anxiety has become an important issue for the application of electric vehicles (EVs). Drivers need information on whether they can reach their destinations and what the remaining capacity would be before starting a trip. In order to satisfy the needs and save computing resources for computing-intense applications in vehicles, we propose a simplified historical-information-based State of Charge (SOC) prediction (SHSP) algorithm. First, definitions of SOC, historical average power, and equivalent current are given. Based on these definitions, Rint-based models of supercapacitors, under constant power and constant current loading, are established respectively. Then, a relationship between the historical average power and the predicted SOC is derived with the help of the equivalent current as a bridge. The experimental results demonstrate that the 35-step-forward SOC prediction error of the driving-behavior-based SOC prediction (SHSP) is close to the driving-behavior-based SOC prediction method (DBSP) and lower than Long-Short-Term-Memory-based SOC prediction method (LSTM). Importantly, the time of running SHSP code is less than that of running DBSP code, and much less than that of running LSTM code.