Stability and safety are the essential factors for commuter electric vehicles despite the battery and charging system. There were many accidents caused by the loss of stability of the vehicle. Stability factors included the dynamic responses of the yaw rate and the side slip angle, which were affected by tire cornering stiffness and air drag coefficient parameters. These were the key general problems in building a sophisticated and reliable advanced dynamics control system for electric vehicles. In this study, the curve fitting least square method was proposed and validated as a way to estimate the optimum value of tire cornering stiffness and air drag coefficients, which greatly affected the stability response of the two-track vehicle model. The dynamic responses generated by the model after applying the optimum estimated parameters were compared to and validated against CarSim simulator results. A double line change procedure used to test and validate the proposed method because vehicles tend to lose their stability during this type of yawing tendency maneuver. The comparison between the model and CarSim resulted in a decrease of RMSE error of the model by 62.26% for side slip, 42.76% for velocity, and 80.44% for yaw rate after applying the tuned parameters. These numbers meant that the optimum estimated coefficient value of tire cornering stiffness and air drag force could be obtained using the least square, and the impact of model error could be mitigated as well.