A driving-behavior-based SoC prediction method for light urban vehicles powered by supercapacitors

Abstract

Range anxiety is one of the problems that hinder the large-scale application of electric vehicles (EVs). We propose a driving-behavior-based State-of-Charge (SoC) prediction (DBSP) algorithm to overcome this problem. This algorithm can determine whether drivers can reach their destinations while also predicting the SoC if drivers were to return the trip. First, two supercapacitor equivalent circuit models are established with one based on the historical average power and the other based on the equivalent current, which is proposed in this algorithm. Then, based on the equivalent transformation of the two models, an analytical expression relating the historical average power and the predicted SoC is derived by using the equivalent current as a “bridge.” Therefore, the predicted SoC can be dynamically adjusted in response to recorded historical data, including the output power, speed, and distance of EVs powered by supercapacitors. The simulation results demonstrate that the total prediction error is less than 0.5% of the real SoC at different initial SoC and temperature, which represents idealized behavior-based driving. In contrast, in actual driving experiments, the total prediction error is less than 3% of the real SoC at different initial SoC and temperature.